%pip install fast_ml    ## Required for constant feature identification package

from fast_ml.utilities import display_all
from fast_ml.feature_selection import get_constant_features

Requirement already satisfied: fast_ml in /home/vk/anaconda3/envs/dsbasic/lib/python3.10/site-packages (3.68)
Note: you may need to restart the kernel to use updated packages.

# import all libraries numpy, pandas, matplotlib, seaborn. 
import numpy as np, pandas as pd
import matplotlib.pyplot as plt
import matplotlib
import seaborn as sns; sns.set_theme(color_codes=True)

import warnings
warnings.filterwarnings('ignore')

from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = 'all'
%matplotlib inline

# Set custom display properties in pandas
pd.set_option("display.max_rows", 900)
pd.set_option("display.max_columns", 900) 
pd.set_option('display.float_format', lambda x: '%.3f' % x)

# Import the necessary machine learning packages (sklearn, statsmodel) for performing logistic regression
import statsmodels.api as sm 
from sklearn.feature_selection import RFE
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split, cross_val_score
from statsmodels.stats.outliers_influence import variance_inflation_factor
from sklearn.preprocessing import LabelEncoder, OneHotEncoder, MinMaxScaler, StandardScaler
from sklearn.metrics import accuracy_score, recall_score,precision_score, roc_auc_score, confusion_matrix, f1_score, roc_curve, precision_recall_curve

# Import the dataset from leads.csv
lead_score_df = pd.read_csv('Leads.csv')
lead_score_df.head(1)

lead_score_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 9240 entries, 0 to 9239
Data columns (total 37 columns):
 #   Column                                         Non-Null Count  Dtype  
---  ------                                         --------------  -----  
 0   Prospect ID                                    9240 non-null   object 
 1   Lead Number                                    9240 non-null   int64  
 2   Lead Origin                                    9240 non-null   object 
 3   Lead Source                                    9204 non-null   object 
 4   Do Not Email                                   9240 non-null   object 
 5   Do Not Call                                    9240 non-null   object 
 6   Converted                                      9240 non-null   int64  
 7   TotalVisits                                    9103 non-null   float64
 8   Total Time Spent on Website                    9240 non-null   int64  
 9   Page Views Per Visit                           9103 non-null   float64
 10  Last Activity                                  9137 non-null   object 
 11  Country                                        6779 non-null   object 
 12  Specialization                                 7802 non-null   object 
 13  How did you hear about X Education             7033 non-null   object 
 14  What is your current occupation                6550 non-null   object 
 15  What matters most to you in choosing a course  6531 non-null   object 
 16  Search                                         9240 non-null   object 
 17  Magazine                                       9240 non-null   object 
 18  Newspaper Article                              9240 non-null   object 
 19  X Education Forums                             9240 non-null   object 
 20  Newspaper                                      9240 non-null   object 
 21  Digital Advertisement                          9240 non-null   object 
 22  Through Recommendations                        9240 non-null   object 
 23  Receive More Updates About Our Courses         9240 non-null   object 
 24  Tags                                           5887 non-null   object 
 25  Lead Quality                                   4473 non-null   object 
 26  Update me on Supply Chain Content              9240 non-null   object 
 27  Get updates on DM Content                      9240 non-null   object 
 28  Lead Profile                                   6531 non-null   object 
 29  City                                           7820 non-null   object 
 30  Asymmetrique Activity Index                    5022 non-null   object 
 31  Asymmetrique Profile Index                     5022 non-null   object 
 32  Asymmetrique Activity Score                    5022 non-null   float64
 33  Asymmetrique Profile Score                     5022 non-null   float64
 34  I agree to pay the amount through cheque       9240 non-null   object 
 35  A free copy of Mastering The Interview         9240 non-null   object 
 36  Last Notable Activity                          9240 non-null   object 
dtypes: float64(4), int64(3), object(30)
memory usage: 2.6+ MB

# To distinguish numerical columns either as categorical/discrete or non categorical and return as dict
def classify_feature_dtype(df, cols):
    d_categories = {'int_cat': [], "float_ts":[] }
    for col in cols:
        if (len(df[col].unique()) < 20) and (df[col].dtype != np.float64):
            d_categories['int_cat'].append(col)
        else:
            
            if not isinstance(df[col][df[col].notna()].unique()[0], str):
                d_categories['float_ts'].append(col)
            else:
                d_categories['int_cat'].append(col)
    return d_categories

# Print all statistical information for a given set of columns
def show_stats(df, cols):
    for col in list(cols):
        print("Total Nulls: {0},\nMode: {1}".format(df[col].isna().sum(), df[col].mode()[0]))
        if len(df[col].unique()) < 50:
            print("\nUnique: {0}\n".format(df[col].unique()))
        if (df[col].dtype == int) or (df[col].dtype == float):
            print("Median   : {0}, \nVariance: {1}, \n\nDescribe: {2} \n".format(df[col].median(), df[col].var(), df[col].describe()))
        print("ValueCounts: {0} \n\n\n".format((df[col].value_counts(normalize=True) * 100).head(5)))
        print("------------------------------------------------------------------")

# Return the percentage of null values in each columns in a dataframe
def check_cols_null_pct(df):
    df_non_na = df.count() / len(df)  # Ratio of non null values
    df_na_pct = (1 - df_non_na) * 100 # Find the Percentage of null values
    return df_na_pct.sort_values(ascending=False) # Sort the resulting values in descending order

# Generates charts based on the data type of the cols, as part of the univariate analysis 
# it takes dataframe, columns, train data 0,1, and feature type as args.
def univariate_plots(df, cols, target=None, ftype=None, l_dict = None):
    for col in cols:
        #generate plots and graphs for category type. (generates piechart, countplot, boxplot / if training data is provided it generates bar chart instead)
        if ftype == "categorical":
            fig, axs = plt.subplots(1, 3, figsize=(20, 6))
 
            col_idx = 0
            axs[col_idx].pie(x=df[col].value_counts().head(12), labels=df[col].value_counts().head(12).index.str[:10], autopct="%1.1f%%", 
                    radius=1, textprops={"fontsize": 10, "color": "Black"}, startangle=90, rotatelabels=False, )
            axs[col_idx].set_title("PieChart of {0}".format(col), y=1); plt.xticks(rotation=45); plt.ylabel("Percentage")
            fig.subplots_adjust(wspace=0.5, hspace=0.3)
            
            col_idx += 1
            sns.countplot(data=df, y=col, order=df[col].value_counts().head(15).index, palette="viridis",  ax=axs[col_idx])
            if (l_dict is not None) and (l_dict.get(col) is not None):
                axs[col_idx].legend([ f'{k} - {v}' for k,v in l_dict[col].items()])
            axs[col_idx].set_title("Countplot of {0}".format(col)); plt.xticks(rotation=45); plt.xlabel(col); plt.ylabel("Count")
            fig.subplots_adjust(wspace=0.5, hspace=0.3)

            col_idx += 1
            ax = sns.barplot(data=df, x=df[col].str[:10], y=target, order=df[col].value_counts().index.str[:10], palette="viridis",  ax=axs[col_idx], errwidth=0)
            for i in ax.containers:
                ax.bar_label(i,)
            axs[col_idx].set_title('Barplot against target'); plt.xticks(rotation=90); plt.xlabel(col)
            fig.subplots_adjust(wspace=0.5, hspace=0.3)

            plt.suptitle("Univariate analysis of {0}".format(col), fontsize=12, y=0.95)
            plt.tight_layout()
            plt.subplots_adjust(top=0.85)
            plt.show();
            plt.clf()

        #generate plots and graphs for numerical types. (generates boxplot, histplot, kdeplot, scatterplot)
        elif ftype == "non_categorical":        
            fig, axs = plt.subplots(1, 4, figsize=(20, 6))
            
            col_idx = 0
            
            sns.boxplot(data=df, y=col, palette="viridis", flierprops=dict(marker="o", markersize=6, markerfacecolor="red", markeredgecolor="black"),
                        medianprops=dict(linestyle="-", linewidth=3, color="#FF9900"), whiskerprops=dict(linestyle="-", linewidth=2, color="black"),
                        capprops=dict(linestyle="-", linewidth=2, color="black"), ax=axs[col_idx])
            axs[col_idx].set_title("Boxplot of {0}".format(col)); plt.xticks(rotation=45); plt.xlabel(col)
            fig.subplots_adjust(wspace=0.5, hspace=0.3)
            
            col_idx += 1

            axs[col_idx].hist(data=df, x=col, label=col)
            axs[col_idx].set_title("Histogram of {0}".format(col)); plt.xticks(rotation=45); plt.xlabel(col)
            fig.subplots_adjust(wspace=0.5, hspace=0.3)
            
            col_idx += 1

            sns.kdeplot(df[col], shade=True, ax=axs[col_idx])
            axs[col_idx].set_title("KDE plot of {0}".format(col)); plt.xticks(rotation=45); plt.xlabel(col)
            fig.subplots_adjust(wspace=0.5, hspace=0.3)
            
            col_idx += 1

            sns.scatterplot(df[col], ax=axs[col_idx])
            axs[col_idx].set_title("Scatterplot of {0}".format(col)); plt.xticks(rotation=45); plt.xlabel(col)
            fig.subplots_adjust(wspace=0.5, hspace=0.3)

            plt.suptitle("Univariate analysis of {0}".format(col), fontsize=12, y=0.95)
            plt.tight_layout()
            plt.subplots_adjust(top=0.85)
            plt.show()
            plt.clf()

# Perform Outlier analysis on the given dataframe.
# Find Lower threshold, Upper threshold and IQR values. 
# Return the Result as a dataframe. 
# find_outlier = True argument: restricts the output df to outlier columns. whereas find_outlier = False: returns results for all columns
def get_extremeval_threshld(df, find_outlier=False):
    outlier_df = pd.DataFrame(columns=[i for i in df.columns if find_outlier == True], data=None)
    
    for col in df.columns:
        thirdq, firstq = df[col].quantile(0.75), df[col].quantile(0.25)
        iqr = 1.5 * (thirdq - firstq)
        extvalhigh, extvallow = iqr + thirdq, firstq - iqr
        
        if find_outlier == True:
            dfout = df.loc[(df[col] > extvalhigh) | (df[col] < extvallow)]
            dfout = dfout.assign(name=col, thresh_low=extvallow, thresh_high=extvalhigh)
        else:
            dfout = pd.DataFrame([[col, extvallow, extvalhigh]], columns=['name', 'thresh_low', 'thresh_high'])
            
        outlier_df = pd.concat([outlier_df, dfout])
    # outlier_df = outlier_df.reset_index(drop=True)
    outlier_df = outlier_df.set_index('name',drop=True)
    return outlier_df

#duplicates row validation by id cols
print(f"{lead_score_df.index.is_unique}, {lead_score_df.columns.is_unique}, {lead_score_df['Prospect ID'].is_unique}, {lead_score_df['Lead Number'].is_unique}")

True, True, True, True

# drop unnecessary columns
lead_score_df = lead_score_df.drop(columns=['Prospect ID','Lead Number', 'I agree to pay the amount through cheque', 'Last Notable Activity'])

# rename columns that are too long
lead_score_df = lead_score_df.rename(columns={'Total Time Spent on Website':'ttime_on_site', 'Page Views Per Visit':'pg_view_pv', 'How did you hear about X Education':'info_abt_X_Edu', 'What is your current occupation':'curr_occupation',
    'What matters most to you in choosing a course':'reason_behind_course', 'Receive More Updates About Our Courses':'more_course_updates', 'Update me on Supply Chain Content':'supply_chain_info', 'Get updates on DM Content':'get_dm',
    'Asymmetrique Activity Index':'asym_activ_idx', 'Asymmetrique Profile Index':'asym_prof_idx', 'Asymmetrique Activity Score':'asym_activ_score', 'Asymmetrique Profile Score':'asym_prof_score',
    'A free copy of Mastering The Interview':'avail_free_copy'})

# replace unnecessary space in columns with underscore and covert it to lower case
lead_score_df.columns = lead_score_df.columns.str.replace(pat=' ',repl='_', regex=True)
lead_score_df.columns = lead_score_df.columns.str.lower()

# Check the shape and size of the data frame
lead_score_df.head(1)
lead_score_df.dtypes

lead_origin                 object
lead_source                 object
do_not_email                object
do_not_call                 object
converted                    int64
totalvisits                float64
ttime_on_site                int64
pg_view_pv                 float64
last_activity               object
country                     object
specialization              object
info_abt_x_edu              object
curr_occupation             object
reason_behind_course        object
search                      object
magazine                    object
newspaper_article           object
x_education_forums          object
newspaper                   object
digital_advertisement       object
through_recommendations     object
more_course_updates         object
tags                        object
lead_quality                object
supply_chain_info           object
get_dm                      object
lead_profile                object
city                        object
asym_activ_idx              object
asym_prof_idx               object
asym_activ_score           float64
asym_prof_score            float64
avail_free_copy             object
dtype: object

print(f'{lead_score_df.shape}, {lead_score_df.size}')

(9240, 33), 304920

# check null val percentage
# After checking the null value percentage for all the features
# We could see that there are many features that have more than 45% of non values

null_pct = check_cols_null_pct(lead_score_df)
null_pct[null_pct > 0]

lead_quality           51.591
asym_prof_score        45.649
asym_activ_score       45.649
asym_prof_idx          45.649
asym_activ_idx         45.649
tags                   36.288
lead_profile           29.318
reason_behind_course   29.318
curr_occupation        29.113
country                26.634
info_abt_x_edu         23.885
specialization         15.563
city                   15.368
pg_view_pv              1.483
totalvisits             1.483
last_activity           1.115
lead_source             0.390
dtype: float64

show_stats(lead_score_df,['tags','specialization'])

Total Nulls: 3353,
Mode: Will revert after reading the email

Unique: ['Interested in other courses' 'Ringing'
 'Will revert after reading the email' nan 'Lost to EINS'
 'In confusion whether part time or DLP' 'Busy' 'switched off'
 'in touch with EINS' 'Already a student' 'Diploma holder (Not Eligible)'
 'Graduation in progress' 'Closed by Horizzon' 'number not provided'
 'opp hangup' 'Not doing further education' 'invalid number'
 'wrong number given' 'Interested  in full time MBA' 'Still Thinking'
 'Lost to Others' 'Shall take in the next coming month' 'Lateral student'
 'Interested in Next batch' 'Recognition issue (DEC approval)'
 'Want to take admission but has financial problems'
 'University not recognized']

ValueCounts: tags
Will revert after reading the email   35.196
Ringing                               20.435
Interested in other courses            8.714
Already a student                      7.899
Closed by Horizzon                     6.081
Name: proportion, dtype: float64 


------------------------------------------------------------------
Total Nulls: 1438,
Mode: Select

Unique: ['Select' 'Business Administration' 'Media and Advertising' nan
 'Supply Chain Management' 'IT Projects Management' 'Finance Management'
 'Travel and Tourism' 'Human Resource Management' 'Marketing Management'
 'Banking, Investment And Insurance' 'International Business' 'E-COMMERCE'
 'Operations Management' 'Retail Management' 'Services Excellence'
 'Hospitality Management' 'Rural and Agribusiness' 'Healthcare Management'
 'E-Business']

ValueCounts: specialization
Select                      24.891
Finance Management          12.510
Human Resource Management   10.869
Marketing Management        10.741
Operations Management        6.447
Name: proportion, dtype: float64 


------------------------------------------------------------------

# replace select string with nan
lead_score_df[['tags','specialization']] = lead_score_df[['tags','specialization']].replace(to_replace=['select','Select',np.nan], value='unknown') ###--------------------->>>>>>>>>>>>>>>
lead_score_df = lead_score_df.replace(to_replace=['select','Select'], value=np.nan) 

# validate select str is replaced
[i for i in lead_score_df.columns if 'select' in (lead_score_df[i].astype(str).str.lower()).str.findall('select').value_counts().index.map(''.join).to_list()]

[]

# check constant features that has only one values
# In the given data set there are a lot of features that have only single value as a category
# These are called as constant features and these features are of little relevance for the machine learning model hence we dropped those features

constant_features = get_constant_features(lead_score_df)
constant_features.head(10)
"','".join(constant_features['Var'].to_list())

"magazine','more_course_updates','supply_chain_info','get_dm','x_education_forums','newspaper','do_not_call','newspaper_article','digital_advertisement','through_recommendations','search"

lead_score_df['reason_behind_course'].value_counts(normalize=True)*100

reason_behind_course
Better Career Prospects     99.954
Flexibility & Convenience    0.031
Other                        0.015
Name: proportion, dtype: float64

# drop all the constant_features
lead_score_df = lead_score_df.drop(['magazine', 'more_course_updates', 'supply_chain_info', 'get_dm', 'x_education_forums', 
                                    'newspaper', 'do_not_call', 'newspaper_article', 'digital_advertisement', 'through_recommendations', 'search','reason_behind_course'], axis=1)

# convert dtypes
# Convert the data type of certain features from object to category type

obj_cols = lead_score_df.select_dtypes(include='object').columns
lead_score_df[obj_cols] = lead_score_df[obj_cols].astype(dtype='category')

# check null val percentage
# Since we have replaced select as null value for certain columns we are seeing increase in the null value percentage for those features
# Therefore we dropped all the features that have more than 40 of null values

null_pct = check_cols_null_pct(lead_score_df)
null_pct[null_pct > 0]
lead_score_df = lead_score_df.drop(null_pct[null_pct > 40].index, axis=1)

info_abt_x_edu     78.463
lead_profile       74.188
lead_quality       51.591
asym_prof_score    45.649
asym_activ_score   45.649
asym_prof_idx      45.649
asym_activ_idx     45.649
city               39.708
curr_occupation    29.113
country            26.634
totalvisits         1.483
pg_view_pv          1.483
last_activity       1.115
lead_source         0.390
dtype: float64

null_pct = check_cols_null_pct(lead_score_df)
null_pct[null_pct > 0]
na_cols = null_pct[null_pct > 0].index
na_cols

city              39.708
curr_occupation   29.113
country           26.634
totalvisits        1.483
pg_view_pv         1.483
last_activity      1.115
lead_source        0.390
dtype: float64

Index(['city', 'curr_occupation', 'country', 'totalvisits', 'pg_view_pv',
       'last_activity', 'lead_source'],
      dtype='object')

lead_score_df.describe(include=np.number)
lead_score_df.describe(exclude=np.number)

# impute missing categorical values using mode, if a particular value in that column has higher frequency say > 50%
for i in lead_score_df.select_dtypes(include='category'):
    temp = lead_score_df[i].value_counts(normalize=True, ascending=False) * 100
    if temp.iloc[0] > 50:
        print(i)
        lead_score_df[i] = lead_score_df[i].fillna(temp.index[0])

lead_origin
do_not_email
country
curr_occupation
city
avail_free_copy

# Now there are only two columns that have more than 36 off missing values tags and specialization

null_pct = check_cols_null_pct(lead_score_df)
null_pct[null_pct > 0]

totalvisits     1.483
pg_view_pv      1.483
last_activity   1.115
lead_source     0.390
dtype: float64

show_stats(lead_score_df, lead_score_df.columns) ###--------------------->>>>>>>>>>>>>>>

Total Nulls: 0,
Mode: Landing Page Submission

Unique: ['API', 'Landing Page Submission', 'Lead Add Form', 'Lead Import', 'Quick Add Form']
Categories (5, object): ['API', 'Landing Page Submission', 'Lead Add Form', 'Lead Import', 'Quick Add Form']

ValueCounts: lead_origin
Landing Page Submission   52.879
API                       38.745
Lead Add Form              7.771
Lead Import                0.595
Quick Add Form             0.011
Name: proportion, dtype: float64 


------------------------------------------------------------------
Total Nulls: 36,
Mode: Google

Unique: ['Olark Chat', 'Organic Search', 'Direct Traffic', 'Google', 'Referral Sites', ..., 'welearnblog_Home', 'youtubechannel', 'testone', 'Press_Release', 'NC_EDM']
Length: 22
Categories (21, object): ['Click2call', 'Direct Traffic', 'Facebook', 'Google', ..., 'google', 'testone', 'welearnblog_Home', 'youtubechannel']

ValueCounts: lead_source
Google           31.160
Direct Traffic   27.629
Olark Chat       19.068
Organic Search   12.538
Reference         5.802
Name: proportion, dtype: float64 


------------------------------------------------------------------
Total Nulls: 0,
Mode: No

Unique: ['No', 'Yes']
Categories (2, object): ['No', 'Yes']

ValueCounts: do_not_email
No    92.056
Yes    7.944
Name: proportion, dtype: float64 


------------------------------------------------------------------
Total Nulls: 0,
Mode: 0

Unique: [0 1]

Median   : 0.0, 
Variance: 0.23689009604963565, 

Describe: count   9240.000
mean       0.385
std        0.487
min        0.000
25%        0.000
50%        0.000
75%        1.000
max        1.000
Name: converted, dtype: float64 

ValueCounts: converted
0   61.461
1   38.539
Name: proportion, dtype: float64 


------------------------------------------------------------------
Total Nulls: 137,
Mode: 0.0

Unique: [  0.   5.   2.   1.   4.   8.  11.   6.   3.   7.  13.  17.  nan   9.
  12.  10.  16.  14.  21.  15.  22.  19.  18.  20.  43.  30.  23.  55.
 141.  25.  27.  29.  24.  28.  26.  74.  41.  54. 115. 251.  32.  42.]

Median   : 3.0, 
Variance: 23.569594711063154, 

Describe: count   9103.000
mean       3.445
std        4.855
min        0.000
25%        1.000
50%        3.000
75%        5.000
max      251.000
Name: totalvisits, dtype: float64 

ValueCounts: totalvisits
0.000   24.047
2.000   18.455
3.000   14.347
4.000   12.304
5.000    8.602
Name: proportion, dtype: float64 


------------------------------------------------------------------
Total Nulls: 0,
Mode: 0
Median   : 248.0, 
Variance: 300327.5275216322, 

Describe: count   9240.000
mean     487.698
std      548.021
min        0.000
25%       12.000
50%      248.000
75%      936.000
max     2272.000
Name: ttime_on_site, dtype: float64 

ValueCounts: ttime_on_site
0     23.734
60     0.206
74     0.195
75     0.195
127    0.195
Name: proportion, dtype: float64 


------------------------------------------------------------------
Total Nulls: 137,
Mode: 0.0
Median   : 2.0, 
Variance: 4.6717267097228925, 

Describe: count   9103.000
mean       2.363
std        2.161
min        0.000
25%        1.000
50%        2.000
75%        3.000
max       55.000
Name: pg_view_pv, dtype: float64 

ValueCounts: pg_view_pv
0.000   24.047
2.000   19.719
3.000   13.139
4.000    9.843
1.000    7.151
Name: proportion, dtype: float64 


------------------------------------------------------------------
Total Nulls: 103,
Mode: Email Opened

Unique: ['Page Visited on Website', 'Email Opened', 'Unreachable', 'Converted to Lead', 'Olark Chat Conversation', ..., 'SMS Sent', 'Visited Booth in Tradeshow', 'Resubscribed to emails', 'Email Received', 'Email Marked Spam']
Length: 18
Categories (17, object): ['Approached upfront', 'Converted to Lead', 'Email Bounced', 'Email Link Clicked', ..., 'Unreachable', 'Unsubscribed', 'View in browser link Clicked', 'Visited Booth in Tradeshow']

ValueCounts: last_activity
Email Opened              37.616
SMS Sent                  30.043
Olark Chat Conversation   10.649
Page Visited on Website    7.004
Converted to Lead          4.684
Name: proportion, dtype: float64 


------------------------------------------------------------------
Total Nulls: 0,
Mode: India

Unique: ['India', 'Russia', 'Kuwait', 'Oman', 'United Arab Emirates', ..., 'Denmark', 'Philippines', 'Bangladesh', 'Vietnam', 'Indonesia']
Length: 38
Categories (38, object): ['Asia/Pacific Region', 'Australia', 'Bahrain', 'Bangladesh', ..., 'United Kingdom', 'United States', 'Vietnam', 'unknown']

ValueCounts: country
India                  96.894
United States           0.747
United Arab Emirates    0.574
Singapore               0.260
Saudi Arabia            0.227
Name: proportion, dtype: float64 


------------------------------------------------------------------
Total Nulls: 0,
Mode: unknown

Unique: ['unknown', 'Business Administration', 'Media and Advertising', 'Supply Chain Management', 'IT Projects Management', ..., 'Services Excellence', 'Hospitality Management', 'Rural and Agribusiness', 'Healthcare Management', 'E-Business']
Length: 19
Categories (19, object): ['Banking, Investment And Insurance', 'Business Administration', 'E-Business', 'E-COMMERCE', ..., 'Services Excellence', 'Supply Chain Management', 'Travel and Tourism', 'unknown']

ValueCounts: specialization
unknown                     36.580
Finance Management          10.563
Human Resource Management    9.177
Marketing Management         9.069
Operations Management        5.444
Name: proportion, dtype: float64 


------------------------------------------------------------------
Total Nulls: 0,
Mode: Unemployed

Unique: ['Unemployed', 'Student', 'Working Professional', 'Businessman', 'Other', 'Housewife']
Categories (6, object): ['Businessman', 'Housewife', 'Other', 'Student', 'Unemployed', 'Working Professional']

ValueCounts: curr_occupation
Unemployed             89.719
Working Professional    7.641
Student                 2.273
Other                   0.173
Housewife               0.108
Name: proportion, dtype: float64 


------------------------------------------------------------------
Total Nulls: 0,
Mode: unknown

Unique: ['Interested in other courses', 'Ringing', 'Will revert after reading the email', 'unknown', 'Lost to EINS', ..., 'Lateral student', 'Interested in Next batch', 'Recognition issue (DEC approval)', 'Want to take admission but has financial prob..., 'University not recognized']
Length: 27
Categories (27, object): ['Already a student', 'Busy', 'Closed by Horizzon', 'Diploma holder (Not Eligible)', ..., 'opp hangup', 'switched off', 'unknown', 'wrong number given']

ValueCounts: tags
unknown                               36.288
Will revert after reading the email   22.424
Ringing                               13.019
Interested in other courses            5.552
Already a student                      5.032
Name: proportion, dtype: float64 


------------------------------------------------------------------
Total Nulls: 0,
Mode: Mumbai

Unique: ['Mumbai', 'Thane & Outskirts', 'Other Metro Cities', 'Other Cities', 'Other Cities of Maharashtra', 'Tier II Cities']
Categories (6, object): ['Mumbai', 'Other Cities', 'Other Cities of Maharashtra', 'Other Metro Cities', 'Thane & Outskirts', 'Tier II Cities']

ValueCounts: city
Mumbai                        74.578
Thane & Outskirts              8.139
Other Cities                   7.424
Other Cities of Maharashtra    4.946
Other Metro Cities             4.113
Name: proportion, dtype: float64 


------------------------------------------------------------------
Total Nulls: 0,
Mode: No

Unique: ['No', 'Yes']
Categories (2, object): ['No', 'Yes']

ValueCounts: avail_free_copy
No    68.745
Yes   31.255
Name: proportion, dtype: float64 


------------------------------------------------------------------

# univariate plots#
# Now we perform Univariate analysis on both categorical and numerical variables
dtype_dict = classify_feature_dtype(lead_score_df, lead_score_df.columns )
univariate_plots(lead_score_df, dtype_dict['float_ts'], ftype='non_categorical', target='converted')

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# univariate plots
cols = dtype_dict['int_cat'].copy()
cols.remove('converted')
univariate_plots(lead_score_df, cols, ftype='categorical', target='converted')

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# Bivariate plots
# The pair plot view shows that there is a slight correlation between total visits and page view per visit

sns.pairplot(lead_score_df)
plt.show();

# multivariate plots
# The heat map shows that majority of the features have no correlation or has lesser than 0 5 correlation

plt.figure(figsize = (20, 4)) # Size of the figure
sns.heatmap(lead_score_df.select_dtypes(exclude='category').corr(), annot = True)
plt.show();

# # Boxplots - numerical features against target

axs = 141
plt.figure(figsize=(26, 6))
for i in list(set(dtype_dict['float_ts']) - set(['date','converted','lead_number'])):
    plt.subplot(axs)
    sns.boxplot(y=i, x='converted', data=lead_score_df, palette='tab10')
    axs += 1
plt.show();

# # Generate Bivariate Boxplots combinations for all the categorical vs continuous columns

x_lst = list(dtype_dict['int_cat']) # x_list variable contains all the categorical Columns 
y_lst = list(dtype_dict['float_ts'])# y_list contains all Continuous feature type columns 
axs = 1
for x_col in x_lst:
    plt.figure(figsize=(26,72))
    for y_col in y_lst:
        plt.subplot(18,4,axs)
        sns.boxplot(x=x_col, y=y_col, data=lead_score_df, palette='tab10')
        plt.xticks(rotation=90)
        axs += 1
    plt.show();
    axs = 1
plt.show();

# # Generate Bivariate Barplots combinations for all the categorical features

# matplotlib.rcParams['ytick.labelsize'] = 8
# matplotlib.rcParams['xtick.labelsize'] = 8

# y_lst = x_lst = list(set(dtype_dict['int_cat']) - set(['converted','lead_number'])) # x_list variable contains all the categorical Columns 
# axs = 1
# for x_col in x_lst:
#     plt.figure(figsize=(25,400))
#     for y_col in y_lst:
        
#         # x_f = lead_score_df[x_col]
#         # y_f = lead_score_df[y_col]
            
#         # if isinstance(x_f,(object,'category')):
#         #     x_f = x_f.astype('str',copy=True).str.slice(0,10)
#         # if isinstance(y_f,(object,'category')):
#         #     y_f = y_f.astype('str',copy=True).str.slice(0,10)
        
#         plt.subplot(60,4,axs)
#         sns.barplot(x=x_col, y=y_col, hue ='converted' ,data=lead_score_df, palette='tab10')
#         plt.xticks(rotation=90)
#         # plt.ylabel(ylabel= lead_score_df[y_col].name, labelpad=-50)
#         # plt.locator_params(tight=True)
#         axs += 1
#     plt.show();
#     axs = 1
# plt.show();

# # Multivariate Boxplots  - 

# matplotlib.rcParams['ytick.labelsize'] = 8
# matplotlib.rcParams['xtick.labelsize'] = 8
# # matplotlib.rcParams['ytick.major.size'] = 2
# # matplotlib.rcParams['xtick.major.size'] = 2

# z_lst = x_lst = list(dtype_dict['int_cat'])
# y_lst = list(dtype_dict['float_ts'])

# axs = 1
# for x_col in x_lst:
#     for y_col in y_lst:
#         plt.figure(figsize=(25, 200))
#         for z_col in z_lst:
            
#             # x_f = lead_score_df[x_col]
#             # y_f = lead_score_df[y_col]
            
#             # if isinstance(x_f,(object,'category')):
#             #     x_f = x_f.astype('str',copy=True).str.slice(0,10)
#             # if isinstance(y_f,(object,'category')):
#             #     y_f = y_f.astype('str',copy=True).str.slice(0,10)
            
#             plt.subplot(25, 2, axs)
#             sns.boxplot(x=x_col, y=y_col, hue=z_col,hue_order=lead_score_df[z_col].value_counts().head(10).index ,data=lead_score_df, palette='tab10')
#             plt.xticks(rotation=90)
#             # plt.locator_params(tight=True)
#             plt.legend(loc='best')
#             axs += 1
#         plt.show();
#     axs = 1
#     print("--------------------------------------------------------------------------")
#     # break
# plt.show();

# # Multivariate Boxplots  - 

# matplotlib.rcParams['ytick.labelsize'] = 8
# matplotlib.rcParams['xtick.labelsize'] = 8
# # matplotlib.rcParams['ytick.major.size'] = 2
# # matplotlib.rcParams['xtick.major.size'] = 2

# x_lst = list(dtype_dict['int_cat'])
# x_lst.remove('lead_origin')
# x_lst.remove('converted')
# z_lst = y_lst= x_lst
# axs = 1
# for x_col in x_lst:
#     for y_col in y_lst:
#             plt.figure(figsize=(25, 500))
#             for z_col in z_lst:
#                  if (x_col != y_col) & (y_col != z_col) & (x_col != z_col):
                         
#                 #     x_f = lead_score_df[x_col]
#                 #     y_f = lead_score_df[y_col]
#                 #     if isinstance(x_f,(object,'category')):
#                 #             x_f = x_f.astype('str',copy=True).str.slice(0,10)
#                 #     if isinstance(y_f,(object,'category')):
#                 #             y_f = y_f.astype('str',copy=True).str.slice(0,10)
                            
#                     plt.subplot(100, 3, axs)
#                     sns.barplot(x=x_col, y=y_col, hue=z_col, hue_order=lead_score_df[z_col].value_counts().head(10).index, data=lead_score_df, palette='tab10')
#                     plt.xticks(rotation=90)
#                 #     plt.ylabel(labelpad=3)
#                 #     plt.locator_params.set_label_coords(-0.01, 0.5)
#                     plt.legend(loc='best')
#                     axs += 1
#             plt.show()
#             axs = 1
#     print("--------------------------------------------------------------------------")
# plt.show();

# After performing eda we identified outliers in page view per visit totalvisits ttime on site columns we therefore cap all the values to the upper cut off and lower cut off of the iqr range

ex_val_df = get_extremeval_threshld(df=lead_score_df.select_dtypes(exclude=['category','object']) )
ex_val_df
lead_score_df.describe(percentiles=[.05,.1,.2,.5,.8,.9])

# Fix Outliers by setting either thresh low or thresh low for both extremes 
lower_cutoff = ex_val_df.loc['pg_view_pv','thresh_low']
lead_score_df['pg_view_pv'] = np.where((lead_score_df['pg_view_pv'] < lower_cutoff), lower_cutoff, lead_score_df['pg_view_pv'])
upper_cutoff = ex_val_df.loc['pg_view_pv','thresh_high']
lead_score_df['pg_view_pv'] = np.where((lead_score_df['pg_view_pv'] > upper_cutoff), upper_cutoff, lead_score_df['pg_view_pv'])

# Fix Outliers by setting either thresh low or thresh low for both extremes
lower_cutoff = ex_val_df.loc['totalvisits','thresh_low']
lead_score_df['totalvisits'] = np.where((lead_score_df['totalvisits'] < lower_cutoff), lower_cutoff, lead_score_df['totalvisits'])
upper_cutoff = ex_val_df.loc['totalvisits','thresh_high']
lead_score_df['totalvisits'] = np.where((lead_score_df['totalvisits'] > upper_cutoff), upper_cutoff, lead_score_df['totalvisits'])

# Fix Outliers by setting either thresh low or thresh low for both extremes
lower_cutoff = ex_val_df.loc['ttime_on_site','thresh_low']
lead_score_df['ttime_on_site'] = np.where((lead_score_df['ttime_on_site'] < lower_cutoff), lower_cutoff, lead_score_df['ttime_on_site'])
upper_cutoff = ex_val_df.loc['ttime_on_site','thresh_high']
lead_score_df['ttime_on_site'] = np.where((lead_score_df['ttime_on_site'] > upper_cutoff), upper_cutoff, lead_score_df['ttime_on_site'])

lead_score_df.describe()

# Additionally there are few null values in their total visits and page view pub features therefore we replace with the mean of that particular column

lead_score_df['totalvisits'] = lead_score_df['totalvisits'].replace(to_replace=np.nan, value=lead_score_df['totalvisits'].median())
lead_score_df['pg_view_pv'] = lead_score_df['pg_view_pv'].replace(to_replace=np.nan, value=lead_score_df['pg_view_pv'].median())

# our null values have significantly reduced
null_pct = check_cols_null_pct(lead_score_df)
null_pct[null_pct>0]
lead_score_df.shape

last_activity   1.115
lead_source     0.390
dtype: float64

(9240, 14)

lead_score_df = lead_score_df.dropna(subset=['last_activity','lead_source'])

# lead_score_df.to_csv('spec_tag_analysis.csv')

lead_score_df = lead_score_df.drop(['country'], axis=1)
null_pct = check_cols_null_pct(lead_score_df)
null_pct[null_pct>0] ### there are no null values

lead_score_df.shape

Series([], dtype: float64)

(9103, 13)

lead_score_df.dtypes

lead_origin        category
lead_source        category
do_not_email       category
converted             int64
totalvisits         float64
ttime_on_site       float64
pg_view_pv          float64
last_activity      category
specialization     category
curr_occupation    category
tags               category
city               category
avail_free_copy    category
dtype: object

not lead_score_df['totalvisits'].dtype == np.float64

False

dtype_dict = classify_feature_dtype(lead_score_df, lead_score_df.columns )
univariate_plots(lead_score_df, dtype_dict['float_ts'], ftype='non_categorical', target='converted')

cols = dtype_dict['int_cat'].copy()
cols.remove('converted')
univariate_plots(lead_score_df, cols, ftype='categorical', target='converted')

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# replace Yes, No with 1 and 0
lead_score_df = lead_score_df.replace(to_replace=['Yes', 'No'], value=[1, 0])

# Data Imbalance
# From the target variable we have found out the imbalance ratios around 62 therefore we decide not to rebalance

imbalance_ratio = sum(lead_score_df['converted'] == 1)/sum(lead_score_df['converted'] == 0) * 100
print(f'{round(imbalance_ratio, 2)}%')

61.09%

# Conversion Ratio 
# From the target variable the conversion ratio is around 38 it shows that there is a very high probability of failure in conversion

converted = (sum(lead_score_df['converted'])/len(lead_score_df['converted'].index))*100
print(f'{round(converted, 2)}%')

37.92%

# errorline # do no remove this line

#  we perform dummy encoding
new_ls_df = pd.get_dummies(lead_score_df, columns=lead_score_df.select_dtypes('category').columns.difference(['tags','specialization']), drop_first=True, dtype=float)
new_ls_df.head(1)
new_ls_df.shape

(9103, 58)

new_ls_df = pd.get_dummies(new_ls_df, columns=['tags','specialization'], dtype=float)
new_ls_df = new_ls_df.drop(['tags_unknown','specialization_unknown'], axis=1)
new_ls_df.shape

(9103, 100)

X = new_ls_df.drop(['converted'], axis=1)
y = new_ls_df['converted']

# Now we split the dataset into train and test set
np.random.seed(0)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0)

new_ls_df.dtypes

converted                                                   int64
totalvisits                                               float64
ttime_on_site                                             float64
pg_view_pv                                                float64
avail_free_copy_1                                         float64
city_Other Cities                                         float64
city_Other Cities of Maharashtra                          float64
city_Other Metro Cities                                   float64
city_Thane & Outskirts                                    float64
city_Tier II Cities                                       float64
curr_occupation_Housewife                                 float64
curr_occupation_Other                                     float64
curr_occupation_Student                                   float64
curr_occupation_Unemployed                                float64
curr_occupation_Working Professional                      float64
do_not_email_1                                            float64
last_activity_Converted to Lead                           float64
last_activity_Email Bounced                               float64
last_activity_Email Link Clicked                          float64
last_activity_Email Marked Spam                           float64
last_activity_Email Opened                                float64
last_activity_Email Received                              float64
last_activity_Form Submitted on Website                   float64
last_activity_Had a Phone Conversation                    float64
last_activity_Olark Chat Conversation                     float64
last_activity_Page Visited on Website                     float64
last_activity_Resubscribed to emails                      float64
last_activity_SMS Sent                                    float64
last_activity_Unreachable                                 float64
last_activity_Unsubscribed                                float64
last_activity_View in browser link Clicked                float64
last_activity_Visited Booth in Tradeshow                  float64
lead_origin_Landing Page Submission                       float64
lead_origin_Lead Add Form                                 float64
lead_origin_Lead Import                                   float64
lead_origin_Quick Add Form                                float64
lead_source_Direct Traffic                                float64
lead_source_Facebook                                      float64
lead_source_Google                                        float64
lead_source_Live Chat                                     float64
lead_source_NC_EDM                                        float64
lead_source_Olark Chat                                    float64
lead_source_Organic Search                                float64
lead_source_Pay per Click Ads                             float64
lead_source_Press_Release                                 float64
lead_source_Reference                                     float64
lead_source_Referral Sites                                float64
lead_source_Social Media                                  float64
lead_source_WeLearn                                       float64
lead_source_Welingak Website                              float64
lead_source_bing                                          float64
lead_source_blog                                          float64
lead_source_google                                        float64
lead_source_testone                                       float64
lead_source_welearnblog_Home                              float64
lead_source_youtubechannel                                float64
tags_Already a student                                    float64
tags_Busy                                                 float64
tags_Closed by Horizzon                                   float64
tags_Diploma holder (Not Eligible)                        float64
tags_Graduation in progress                               float64
tags_In confusion whether part time or DLP                float64
tags_Interested  in full time MBA                         float64
tags_Interested in Next batch                             float64
tags_Interested in other courses                          float64
tags_Lateral student                                      float64
tags_Lost to EINS                                         float64
tags_Lost to Others                                       float64
tags_Not doing further education                          float64
tags_Recognition issue (DEC approval)                     float64
tags_Ringing                                              float64
tags_Shall take in the next coming month                  float64
tags_Still Thinking                                       float64
tags_University not recognized                            float64
tags_Want to take admission but has financial problems    float64
tags_Will revert after reading the email                  float64
tags_in touch with EINS                                   float64
tags_invalid number                                       float64
tags_number not provided                                  float64
tags_opp hangup                                           float64
tags_switched off                                         float64
tags_wrong number given                                   float64
specialization_Banking, Investment And Insurance          float64
specialization_Business Administration                    float64
specialization_E-Business                                 float64
specialization_E-COMMERCE                                 float64
specialization_Finance Management                         float64
specialization_Healthcare Management                      float64
specialization_Hospitality Management                     float64
specialization_Human Resource Management                  float64
specialization_IT Projects Management                     float64
specialization_International Business                     float64
specialization_Marketing Management                       float64
specialization_Media and Advertising                      float64
specialization_Operations Management                      float64
specialization_Retail Management                          float64
specialization_Rural and Agribusiness                     float64
specialization_Services Excellence                        float64
specialization_Supply Chain Management                    float64
specialization_Travel and Tourism                         float64
dtype: object

# Post split we perform standard scaling they fit and transform the train data set
# to_scale = ['lead_number', 'totalvisits', 'ttime_on_site', 'pg_view_pv']

to_scale = list(X.columns)

scaler = StandardScaler()
X_train[to_scale] = scaler.fit_transform(X_train[to_scale], y_train)
X_train.head(5)

# We create custom functions for model veiling since iteration we reuse certain functions again and again
# Train and predict function trains the model and predicts on the same data and returns the model its probability and predicted values based on cutoff
# The matrix function returns confusion matrix and accuracy score
# The vif function returns the vif score for the features

def logreg_train_pred_fn(fX_train, fy_train, fcol, fcutoff):
    fX_train_sm = sm.add_constant(fX_train[fcol])
    flogm = sm.GLM(fy_train, fX_train_sm, family = sm.families.Binomial())
    fres = flogm.fit()
    fy_train_pred = fres.predict(fX_train_sm)
    fy_train_pred = fy_train_pred.values.reshape(-1)
    fy_train_pred_final = pd.DataFrame({'Converted':fy_train.values, 'Conv_Prob':fy_train_pred})
    fy_train_pred_final['ID'] = fy_train.index
    fy_train_pred_final['predicted'] = fy_train_pred_final.Conv_Prob.map(lambda x: 1 if x > fcutoff else 0)
    return fres, fy_train_pred,fy_train_pred_final

def logreg_metrics_fn(fy_train_pred_final):
    fconfusion = confusion_matrix(fy_train_pred_final.Converted, fy_train_pred_final.predicted )
    faccuracy = accuracy_score(fy_train_pred_final.Converted, fy_train_pred_final.predicted)
    return fconfusion, faccuracy
   
def logreg_VIF_score_fn(fX_train, fcol):
    fvif = pd.DataFrame()
    fvif['Features'] = fX_train[fcol].columns
    fvif['VIF'] = [variance_inflation_factor(fX_train[fcol].values, i) for i in range(fX_train[fcol].shape[1])]
    fvif['VIF'] = round(fvif['VIF'], 2)
    fvif = fvif.sort_values(by = "VIF", ascending = False)
    return fvif

# Logistic regression model
logm1 = sm.GLM(y_train,(sm.add_constant(X_train)), family = sm.families.Binomial())
res = logm1.fit()
res.summary()

# Since the data set has a lot of features we perform rfe to eliminate insignificant features
logreg = LogisticRegression()

rfe = RFE(estimator=logreg, n_features_to_select=15)             # running RFE with 15 variables as output
rfe = rfe.fit(X_train, y_train)
rfe_feature_Ranking = list(zip(X_train.columns, rfe.support_, rfe.ranking_))

rfe_sorted = sorted(rfe_feature_Ranking, key=lambda x : x[2])
rfe_sorted[:15]

[('ttime_on_site', True, 1),
 ('last_activity_Olark Chat Conversation', True, 1),
 ('last_activity_SMS Sent', True, 1),
 ('lead_origin_Lead Add Form', True, 1),
 ('lead_source_Olark Chat', True, 1),
 ('lead_source_Welingak Website', True, 1),
 ('tags_Already a student', True, 1),
 ('tags_Closed by Horizzon', True, 1),
 ('tags_Interested in other courses', True, 1),
 ('tags_Lost to EINS', True, 1),
 ('tags_Not doing further education', True, 1),
 ('tags_Ringing', True, 1),
 ('tags_Will revert after reading the email', True, 1),
 ('tags_switched off', True, 1),
 ('tags_wrong number given', True, 1)]

col = X_train.columns[rfe.support_]
# X_train.columns[~rfe.support_]

# Now we perform model iteration as many times as possible till we get an optimum result

cutoff = 0.5
res, y_train_pred,y_train_pred_final = logreg_train_pred_fn(X_train, y_train, col, cutoff)
confusion, accuracy = logreg_metrics_fn(y_train_pred_final)
vif = logreg_VIF_score_fn(X_train, col)

print('Model Summary:')          # Model Summary:
res.summary()
print('\nVIF Score:')            # VIF Score:
vif

# print('\nY_Predicted Values:')   # Y_Predicted Values:
# y_train_pred
# print('\nY_Predicted Cutoff:')   # Y_Predicted Cutoff:
# y_train_pred_final
# print('\nConfusion Matrix:')     # Confusion Matrix: 
# confusion
# print(f'\nAccuracy Score: {accuracy}\n')       # Accuracy Score:

Model Summary:

VIF Score:

# col = col.drop('curr_occupation_Housewife', 1)
# col

col = col.drop('tags_wrong number given', 1)
col

cutoff = 0.5
res, y_train_pred,y_train_pred_final = logreg_train_pred_fn(X_train, y_train, col, cutoff)
confusion, accuracy = logreg_metrics_fn(y_train_pred_final)
vif = logreg_VIF_score_fn(X_train, col)

print('Model Summary:')          # Model Summary:
res.summary()
print('\nVIF Score:')            # VIF Score:
vif

# print('\nY_Predicted Values:')   # Y_Predicted Values:
# y_train_pred
# print('\nY_Predicted Cutoff:')   # Y_Predicted Cutoff:
# y_train_pred_final

print('\nConfusion Matrix:')     # Confusion Matrix: 
confusion
print(f'\nAccuracy Score: {accuracy}\n')   # Accuracy Score:

Index(['ttime_on_site', 'last_activity_Olark Chat Conversation',
       'last_activity_SMS Sent', 'lead_origin_Lead Add Form',
       'lead_source_Olark Chat', 'lead_source_Welingak Website',
       'tags_Already a student', 'tags_Closed by Horizzon',
       'tags_Interested in other courses', 'tags_Lost to EINS',
       'tags_Not doing further education', 'tags_Ringing',
       'tags_Will revert after reading the email', 'tags_switched off'],
      dtype='object')

Model Summary:

VIF Score:

Confusion Matrix:

array([[4302,  204],
       [ 389, 2387]])

Accuracy Score: 0.9185663279318869

TP = confusion[1,1] # true positive 
TN = confusion[0,0] # true negatives
FP = confusion[0,1] # false positives
FN = confusion[1,0] # false negatives

# Calculate the sensitivity
TP/(TP+FN)

# Calculate the specificity
TN/(TN+FP)

0.8598703170028819

0.9547270306258322

# Let's create columns with different probability cutoffs 
numbers = [float(x)/10 for x in range(10)]
for i in numbers:
    y_train_pred_final[i]= y_train_pred_final.Conv_Prob.map(lambda x: 1 if x > i else 0)
y_train_pred_final.head()

# Now let's calculate accuracy sensitivity and specificity for various probability cutoffs.
cutoff_df = pd.DataFrame( columns = ['prob','accuracy','sensi','speci'])

num = [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]
for i in num:
    cm1 = confusion_matrix(y_train_pred_final.Converted, y_train_pred_final[i] )
    total1=sum(sum(cm1))
    accuracy = (cm1[0,0]+cm1[1,1])/total1
    
    speci = cm1[0,0]/(cm1[0,0]+cm1[0,1])
    sensi = cm1[1,1]/(cm1[1,0]+cm1[1,1])
    cutoff_df.loc[i] =[ i ,accuracy,sensi,speci]
print(cutoff_df)

       prob  accuracy  sensi  speci
0.000 0.000     0.381  1.000  0.000
0.100 0.100     0.832  0.970  0.747
0.200 0.200     0.884  0.930  0.856
0.300 0.300     0.904  0.900  0.906
0.400 0.400     0.916  0.875  0.942
0.500 0.500     0.919  0.860  0.955
0.600 0.600     0.907  0.807  0.968
0.700 0.700     0.902  0.785  0.975
0.800 0.800     0.896  0.755  0.982
0.900 0.900     0.874  0.686  0.990

# Let's plot accuracy sensitivity and specificity for various probabilities.
cutoff_df.plot.line(x='prob', y=['accuracy','sensi','speci'])
plt.show()

<Axes: xlabel='prob'>

y_train_pred_final['final_predicted'] = y_train_pred_final.Conv_Prob.map( lambda x: 1 if x > 0.29 else 0)
y_train_pred_final.head()

# Let's check the overall accuracy.
accuracy_score(y_train_pred_final.Converted, y_train_pred_final.final_predicted)

0.9026366382861851

confusion = confusion_matrix(y_train_pred_final.Converted, y_train_pred_final.final_predicted )
confusion

array([[4069,  437],
       [ 272, 2504]])

TP = confusion[1,1] # true positive 
TN = confusion[0,0] # true negatives
FP = confusion[0,1] # false positives
FN = confusion[1,0] # false negatives

# Calculate the sensitivity
TP/(TP+FN)

# Calculate the specificity
TN/(TN+FP)

precision_score(y_train_pred_final.Converted, y_train_pred_final.predicted)
recall_score(y_train_pred_final.Converted, y_train_pred_final.predicted)

0.9020172910662824

0.9030181979582779

0.9212659204940178

0.8598703170028819

def draw_roc( actual, probs ):
    fpr, tpr, thresholds = roc_curve( actual, probs, drop_intermediate = False )
    auc_score = roc_auc_score( actual, probs )
    plt.figure(figsize=(5, 5))
    plt.plot( fpr, tpr, label='ROC curve (area = %0.2f)' % auc_score )
    plt.plot([0, 1], [0, 1], 'k--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate or [1 - True Negative Rate]')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic')
    plt.legend(loc="lower right")
    plt.show()
    return None

fpr, tpr, thresholds = roc_curve( y_train_pred_final.Converted, y_train_pred_final.Conv_Prob, drop_intermediate = False )
draw_roc(y_train_pred_final.Converted, y_train_pred_final.Conv_Prob)

precision_score(y_train_pred_final.Converted, y_train_pred_final.predicted)
recall_score(y_train_pred_final.Converted, y_train_pred_final.predicted)

0.9212659204940178

0.8598703170028819

p, r, thresholds = precision_recall_curve(y_train_pred_final.Converted, y_train_pred_final.Conv_Prob)
plt.plot(thresholds, p[:-1], "b")
plt.plot(thresholds, r[:-1], "r")
plt.title('Precision Recall Curve')
plt.show();

y_train_pred_final['final_predicted'] = y_train_pred_final.Conv_Prob.map( lambda x: 1 if x > 0.39 else 0)
y_train_pred_final.head()

# Let's check the overall accuracy.
accuracy_score(y_train_pred_final.Converted, y_train_pred_final.final_predicted)

0.9147212304312002

confusion = confusion_matrix(y_train_pred_final.Converted, y_train_pred_final.final_predicted )
confusion

array([[4229,  277],
       [ 344, 2432]])

TP = confusion[1,1] # true positive 
TN = confusion[0,0] # true negatives
FP = confusion[0,1] # false positives
FN = confusion[1,0] # false negatives

# Calculate the sensitivity
TP/(TP+FN)

# Calculate the specificity
TN/(TN+FP)

precision_score(y_train_pred_final.Converted, y_train_pred_final.predicted)
recall_score(y_train_pred_final.Converted, y_train_pred_final.predicted)

0.8760806916426513

0.9385264092321349

0.9212659204940178

0.8598703170028819

def logreg_test_pred_fn(fX_test, fy_test, fcol, fcutoff, fres):
    fX_test_sm = sm.add_constant(fX_test[fcol])
    fy_test_pred = fres.predict(fX_test_sm)
    fy_test_pred = fy_test_pred.values.reshape(-1)
    fy_test_pred_final = pd.DataFrame({'Converted':fy_test.values, 'Conv_Prob':fy_test_pred})
    fy_test_pred_final['ID'] = fy_test.index
    fy_test_pred_final['predicted'] = fy_test_pred_final.Conv_Prob.map(lambda x: 1 if x > fcutoff else 0)
    return fres, fy_test_pred,fy_test_pred_final

def logreg_test_metrics_fn(fy_test_pred_final):
    fconfusion = confusion_matrix(fy_test_pred_final.Converted, fy_test_pred_final.predicted )
    faccuracy = accuracy_score(fy_test_pred_final.Converted, fy_test_pred_final.predicted)
    return fconfusion, faccuracy
    
def logreg_test_VIF_score_fn(fX_test, fcol):
    fvif = pd.DataFrame()
    fvif['Features'] = fX_test[fcol].columns
    fvif['VIF'] = [variance_inflation_factor(fX_test[fcol].values, i) for i in range(fX_test[fcol].shape[1])]
    fvif['VIF'] = round(fvif['VIF'], 2)
    fvif = fvif.sort_values(by = "VIF", ascending = False)
    return fvif

X_test[to_scale] = scaler.transform(X_test[to_scale])
X_test[col].head(2)
X_test.shape

(1821, 99)

cutoff = 0.39
res, y_test_pred, y_test_pred_final = logreg_test_pred_fn(X_test, y_test, col, cutoff, res)
confusion, accuracy = logreg_test_metrics_fn(y_test_pred_final)
vif = logreg_test_VIF_score_fn(X_test, col)

# print('Model Summary:')          # Model Summary:
# res.summary()
# print('\nVIF Score:')            # VIF Score:
# vif

# print('\nY_Predicted Values:')   # Y_Predicted Values:
# y_test_pred
# print('\nY_Predicted Cutoff:')   # Y_Predicted Cutoff:
# y_test_pred_final

print('\nConfusion Matrix:')     # Confusion Matrix: 
confusion
print(f'\nAccuracy Score: {accuracy}\n')  # Accuracy Score:

Confusion Matrix:

array([[1080,   65],
       [  61,  615]])

Accuracy Score: 0.9308072487644151

TP = confusion[1,1] # true positive 
TN = confusion[0,0] # true negatives
FP = confusion[0,1] # false positives
FN = confusion[1,0] # false negatives

# Calculate the sensitivity
TP/(TP+FN)

# Calculate the specificity
TN/(TN+FP)

precision_score(y_train_pred_final.Converted, y_train_pred_final.predicted)
recall_score(y_train_pred_final.Converted, y_train_pred_final.predicted)

0.9097633136094675

0.9432314410480349

0.9212659204940178

0.8598703170028819

print(f'\nAccuracy Score: {accuracy}\n')  # Accuracy Score:

Accuracy Score: 0.9308072487644151

# errorline # do no remove this line

# for label_encoding:
# Since there are many categorical features with nominal values 5 we decide to use label encoding for those features

cols_to_le = lead_score_df.select_dtypes('category').columns
cols_to_le

Index(['lead_origin', 'lead_source', 'do_not_email', 'last_activity',
       'specialization', 'curr_occupation', 'tags', 'city', 'avail_free_copy'],
      dtype='object')

df_le = lead_score_df.copy()

le = LabelEncoder()
df_le[cols_to_le] = df_le[cols_to_le].apply(le.fit_transform)

plt.figure(figsize=(10, 6))
sns.heatmap(df_le.corr(), annot=True)

<Figure size 1000x600 with 0 Axes>

<Axes: >

# We then merged the encoder features with the non encoded features and name it as new lsdf

# new_ls_df = lead_score_df[lead_score_df.columns.difference(cols_to_le)]
# new_ls_df = new_ls_df.merge(right=df_le, right_index=True, left_index=True)
df_le.head(3)

# our null values have significantly reduced
null_pct = check_cols_null_pct(df_le)
null_pct[null_pct>0]
df_le.shape

Series([], dtype: float64)

(9103, 13)

# new_ls_df = new_ls_df[(new_ls_df.notna()).all(axis=1)]
# check_cols_null_pct(new_ls_df)
# sorted([f'{i} - {new_ls_df[i].unique()}' for i in new_ls_df.columns])

X = df_le.drop(['converted'], axis=1)
y = df_le['converted']

# Now we split the dataset into train and test set
np.random.seed(0)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0)

# Post split we perform standard scaling they fit and transform the train data set

# to_scale = ['lead_number', 'totalvisits', 'ttime_on_site', 'pg_view_pv']
to_scale = ['totalvisits', 'ttime_on_site', 'pg_view_pv', 'lead_source', 'last_activity', 'curr_occupation']

scaler = StandardScaler()
X_train[to_scale] = scaler.fit_transform(X_train[to_scale],y_train)
X_train.head()

# We create custom functions for model veiling since iteration we reuse certain functions again and again
# Train and predict function trains the model and predicts on the same data and returns the model its probability and predicted values based on cutoff
# The matrix function returns confusion matrix and accuracy score
# The vif function returns the vif score for the features

def logreg_train_pred_fn(fX_train, fy_train, fcol, fcutoff):
    fX_train_sm = sm.add_constant(fX_train[fcol])
    flogm = sm.GLM(fy_train, fX_train_sm, family = sm.families.Binomial())
    fres = flogm.fit()
    fy_train_pred = fres.predict(fX_train_sm)
    fy_train_pred = fy_train_pred.values.reshape(-1)
    fy_train_pred_final = pd.DataFrame({'Converted':fy_train.values, 'Conv_Prob':fy_train_pred})
    fy_train_pred_final['ID'] = fy_train.index
    fy_train_pred_final['predicted'] = fy_train_pred_final.Conv_Prob.map(lambda x: 1 if x > fcutoff else 0)
    return fres, fy_train_pred,fy_train_pred_final

def logreg_metrics_fn(fy_train_pred_final):
    fconfusion = confusion_matrix(fy_train_pred_final.Converted, fy_train_pred_final.predicted )
    faccuracy = accuracy_score(fy_train_pred_final.Converted, fy_train_pred_final.predicted)
    return fconfusion, faccuracy
    
def logreg_VIF_score_fn(fX_train, fcol):
    fvif = pd.DataFrame()
    fvif['Features'] = fX_train[fcol].columns
    fvif['VIF'] = [variance_inflation_factor(fX_train[fcol].values, i) for i in range(fX_train[fcol].shape[1])]
    fvif['VIF'] = round(fvif['VIF'], 2)
    fvif = fvif.sort_values(by = "VIF", ascending = False)
    return fvif

# Logistic regression model
logm1 = sm.GLM(y_train,(sm.add_constant(X_train)), family = sm.families.Binomial())
res = logm1.fit()
# res.summary()

# Since the data set has a lot of features we perform rfe to eliminate insignificant features

logreg = LogisticRegression()

rfe = RFE(estimator=logreg, n_features_to_select=15)             # running RFE with 15 variables as output
rfe = rfe.fit(X_train, y_train)
rfe_feature_Ranking = list(zip(X_train.columns, rfe.support_, rfe.ranking_))

rfe_sorted = sorted(rfe_feature_Ranking, key=lambda x : x[2])
rfe_sorted[:15]

[('lead_origin', True, 1),
 ('lead_source', True, 1),
 ('do_not_email', True, 1),
 ('totalvisits', True, 1),
 ('ttime_on_site', True, 1),
 ('pg_view_pv', True, 1),
 ('last_activity', True, 1),
 ('specialization', True, 1),
 ('curr_occupation', True, 1),
 ('tags', True, 1),
 ('city', True, 1),
 ('avail_free_copy', True, 1)]

col = X_train.columns[rfe.support_]
# X_train.columns[~rfe.support_]

# Now we perform model iteration as many times as possible till we get an optimum result

cutoff = 0.5
res, y_train_pred,y_train_pred_final = logreg_train_pred_fn(X_train, y_train, col, cutoff)
confusion, accuracy = logreg_metrics_fn(y_train_pred_final)
vif = logreg_VIF_score_fn(X_train, col)

print('Model Summary:')          # Model Summary:
res.summary()
print('\nVIF Score:')            # VIF Score:
vif

# print('\nY_Predicted Values:')   # Y_Predicted Values:
# y_train_pred
# print('\nY_Predicted Cutoff:')   # Y_Predicted Cutoff:
# y_train_pred_final

# print('\nConfusion Matrix:')     # Confusion Matrix: 
# confusion
# print(f'\nAccuracy Score: {accuracy}\n')       # Accuracy Score:

Model Summary:

VIF Score:

# col = col.drop('avail_free_copy_1', 1)
# col

col = col.drop('city', 1)
col

cutoff = 0.5
res, y_train_pred,y_train_pred_final = logreg_train_pred_fn(X_train, y_train, col, cutoff)
confusion, accuracy = logreg_metrics_fn(y_train_pred_final)
vif = logreg_VIF_score_fn(X_train, col)

print('Model Summary:')          # Model Summary:
res.summary()
print('\nVIF Score:')            # VIF Score:
vif

# print('\nY_Predicted Values:')   # Y_Predicted Values:
# y_train_pred
# print('\nY_Predicted Cutoff:')   # Y_Predicted Cutoff:
# y_train_pred_final

print('\nConfusion Matrix:')     # Confusion Matrix: 
confusion
print(f'\nAccuracy Score: {accuracy}\n')   # Accuracy Score:

Index(['lead_origin', 'lead_source', 'do_not_email', 'totalvisits',
       'ttime_on_site', 'pg_view_pv', 'last_activity', 'specialization',
       'curr_occupation', 'tags', 'avail_free_copy'],
      dtype='object')

Model Summary:

VIF Score:

Confusion Matrix:

array([[3941,  565],
       [1047, 1729]])

Accuracy Score: 0.7786322438890415

TP = confusion[1,1] # true positive 
TN = confusion[0,0] # true negatives
FP = confusion[0,1] # false positives
FN = confusion[1,0] # false negatives

# Calculate the sensitivity
TP/(TP+FN)

# Calculate the specificity
TN/(TN+FP)

0.6228386167146974

0.8746116289391922

# Let's create columns with different probability cutoffs 
numbers = [float(x)/10 for x in range(10)]
for i in numbers:
    y_train_pred_final[i]= y_train_pred_final.Conv_Prob.map(lambda x: 1 if x > i else 0)
y_train_pred_final.head()

# Now let's calculate accuracy sensitivity and specificity for various probability cutoffs.
cutoff_df = pd.DataFrame( columns = ['prob','accuracy','sensi','speci'])

num = [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]
for i in num:
    cm1 = confusion_matrix(y_train_pred_final.Converted, y_train_pred_final[i] )
    total1=sum(sum(cm1))
    accuracy = (cm1[0,0]+cm1[1,1])/total1
    
    speci = cm1[0,0]/(cm1[0,0]+cm1[0,1])
    sensi = cm1[1,1]/(cm1[1,0]+cm1[1,1])
    cutoff_df.loc[i] =[ i ,accuracy,sensi,speci]
print(cutoff_df)

       prob  accuracy  sensi  speci
0.000 0.000     0.381  1.000  0.000
0.100 0.100     0.492  0.974  0.194
0.200 0.200     0.642  0.885  0.492
0.300 0.300     0.762  0.818  0.727
0.400 0.400     0.779  0.714  0.820
0.500 0.500     0.779  0.623  0.875
0.600 0.600     0.766  0.525  0.914
0.700 0.700     0.740  0.408  0.945
0.800 0.800     0.700  0.261  0.971
0.900 0.900     0.652  0.100  0.993

# Let's plot accuracy sensitivity and specificity for various probabilities.
cutoff_df.plot.line(x='prob', y=['accuracy','sensi','speci'])
plt.show()

<Axes: xlabel='prob'>

y_train_pred_final['final_predicted'] = y_train_pred_final.Conv_Prob.map( lambda x: 1 if x > 0.36 else 0)
y_train_pred_final.head()

# Let's check the overall accuracy.
accuracy_score(y_train_pred_final.Converted, y_train_pred_final.final_predicted)

0.7728645976380115

confusion = confusion_matrix(y_train_pred_final.Converted, y_train_pred_final.final_predicted )
confusion

array([[3564,  942],
       [ 712, 2064]])

TP = confusion[1,1] # true positive 
TN = confusion[0,0] # true negatives
FP = confusion[0,1] # false positives
FN = confusion[1,0] # false negatives

# Calculate the sensitivity
TP/(TP+FN)

# Calculate the specificity
TN/(TN+FP)

0.7435158501440923

0.7909454061251664

def draw_roc( actual, probs ):
    fpr, tpr, thresholds = roc_curve( actual, probs, drop_intermediate = False )
    auc_score = roc_auc_score( actual, probs )
    plt.figure(figsize=(5, 5))
    plt.plot( fpr, tpr, label='ROC curve (area = %0.2f)' % auc_score )
    plt.plot([0, 1], [0, 1], 'k--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate or [1 - True Negative Rate]')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic')
    plt.legend(loc="lower right")
    plt.show()
    return None

fpr, tpr, thresholds = roc_curve( y_train_pred_final.Converted, y_train_pred_final.Conv_Prob, drop_intermediate = False )
draw_roc(y_train_pred_final.Converted, y_train_pred_final.Conv_Prob)

precision_score(y_train_pred_final.Converted, y_train_pred_final.predicted)
recall_score(y_train_pred_final.Converted, y_train_pred_final.predicted)

0.7537053182214473

0.6228386167146974

p, r, thresholds = precision_recall_curve(y_train_pred_final.Converted, y_train_pred_final.Conv_Prob)
plt.plot(thresholds, p[:-1], "b")
plt.plot(thresholds, r[:-1], "r")
plt.title('Precision Recall Curve')
plt.show();

y_train_pred_final['final_predicted'] = y_train_pred_final.Conv_Prob.map( lambda x: 1 if x > 0.40 else 0)
y_train_pred_final.head()

# Let's check the overall accuracy.
accuracy_score(y_train_pred_final.Converted, y_train_pred_final.final_predicted)

0.7794561933534743

confusion = confusion_matrix(y_train_pred_final.Converted, y_train_pred_final.final_predicted )
confusion

array([[3693,  813],
       [ 793, 1983]])

TP = confusion[1,1] # true positive 
TN = confusion[0,0] # true negatives
FP = confusion[0,1] # false positives
FN = confusion[1,0] # false negatives

# Calculate the sensitivity
TP/(TP+FN)

# Calculate the specificity
TN/(TN+FP)

precision_score(y_train_pred_final.Converted, y_train_pred_final.predicted)
recall_score(y_train_pred_final.Converted, y_train_pred_final.predicted)

0.7143371757925072

0.8195739014647138

0.7537053182214473

0.6228386167146974

def logreg_test_pred_fn(fX_test, fy_test, fcol, fcutoff, fres):
    fX_test_sm = sm.add_constant(fX_test[fcol])
    fy_test_pred = fres.predict(fX_test_sm)
    fy_test_pred = fy_test_pred.values.reshape(-1)
    fy_test_pred_final = pd.DataFrame({'Converted':fy_test.values, 'Conv_Prob':fy_test_pred})
    fy_test_pred_final['ID'] = fy_test.index
    fy_test_pred_final['predicted'] = fy_test_pred_final.Conv_Prob.map(lambda x: 1 if x > fcutoff else 0)
    return fres, fy_test_pred,fy_test_pred_final

def logreg_test_metrics_fn(fy_test_pred_final):
    fconfusion = confusion_matrix(fy_test_pred_final.Converted, fy_test_pred_final.predicted )
    faccuracy = accuracy_score(fy_test_pred_final.Converted, fy_test_pred_final.predicted)
    return fconfusion, faccuracy
    
def logreg_test_VIF_score_fn(fX_test, fcol):
    fvif = pd.DataFrame()
    fvif['Features'] = fX_test[fcol].columns
    fvif['VIF'] = [variance_inflation_factor(fX_test[fcol].values, i) for i in range(fX_test[fcol].shape[1])]
    fvif['VIF'] = round(fvif['VIF'], 2)
    fvif = fvif.sort_values(by = "VIF", ascending = False)
    return fvif

X_test[to_scale] = scaler.transform(X_test[to_scale])
X_test[col].head()

cutoff = 0.39
res, y_test_pred, y_test_pred_final = logreg_test_pred_fn(X_test, y_test, col, cutoff, res)
confusion, accuracy = logreg_test_metrics_fn(y_test_pred_final)
vif = logreg_test_VIF_score_fn(X_test, col)

print('Model Summary:')          # Model Summary:
res.summary()
print('\nVIF Score:')            # VIF Score:
vif

# print('\nY_Predicted Values:')   # Y_Predicted Values:
# y_test_pred
# print('\nY_Predicted Cutoff:')   # Y_Predicted Cutoff:
# y_test_pred_final

Model Summary:

VIF Score:

print('\nConfusion Matrix:')     # Confusion Matrix: 
confusion
print(f'\nAccuracy Score: {accuracy}\n')  # Accuracy Score:

Confusion Matrix:

array([[948, 197],
       [195, 481]])

Accuracy Score: 0.7847336628226249

TP = confusion[1,1] # true positive 
TN = confusion[0,0] # true negatives
FP = confusion[0,1] # false positives
FN = confusion[1,0] # false negatives

# Calculate the sensitivity
TP/(TP+FN)

# Calculate the specificity
TN/(TN+FP)

precision_score(y_train_pred_final.Converted, y_train_pred_final.predicted)
recall_score(y_train_pred_final.Converted, y_train_pred_final.predicted)

0.7115384615384616

0.8279475982532751

0.7537053182214473

0.6228386167146974

print(f'\nAccuracy Score: {accuracy}\n')  # Accuracy Score:

Accuracy Score: 0.7847336628226249

# errorline #do not remove

#  we perform dummy encoding

new_ls_df = pd.get_dummies(lead_score_df, columns=lead_score_df.select_dtypes('category').columns.difference(['tags','specialization']), drop_first=True, dtype=float)
new_ls_df.head(1)

new_ls_df = pd.get_dummies(new_ls_df, columns=['tags','specialization'], dtype=float)
new_ls_df = new_ls_df.drop(['tags_unknown','specialization_unknown'], axis=1)
new_ls_df.head(1)

X = new_ls_df.drop(['converted'], axis=1)
y = new_ls_df['converted']

# Now we split the dataset into train and test set
np.random.seed(0)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, test_size=0.2, random_state=0)

new_ls_df.dtypes

converted                                                   int64
totalvisits                                               float64
ttime_on_site                                             float64
pg_view_pv                                                float64
avail_free_copy_1                                         float64
city_Other Cities                                         float64
city_Other Cities of Maharashtra                          float64
city_Other Metro Cities                                   float64
city_Thane & Outskirts                                    float64
city_Tier II Cities                                       float64
curr_occupation_Housewife                                 float64
curr_occupation_Other                                     float64
curr_occupation_Student                                   float64
curr_occupation_Unemployed                                float64
curr_occupation_Working Professional                      float64
do_not_email_1                                            float64
last_activity_Converted to Lead                           float64
last_activity_Email Bounced                               float64
last_activity_Email Link Clicked                          float64
last_activity_Email Marked Spam                           float64
last_activity_Email Opened                                float64
last_activity_Email Received                              float64
last_activity_Form Submitted on Website                   float64
last_activity_Had a Phone Conversation                    float64
last_activity_Olark Chat Conversation                     float64
last_activity_Page Visited on Website                     float64
last_activity_Resubscribed to emails                      float64
last_activity_SMS Sent                                    float64
last_activity_Unreachable                                 float64
last_activity_Unsubscribed                                float64
last_activity_View in browser link Clicked                float64
last_activity_Visited Booth in Tradeshow                  float64
lead_origin_Landing Page Submission                       float64
lead_origin_Lead Add Form                                 float64
lead_origin_Lead Import                                   float64
lead_origin_Quick Add Form                                float64
lead_source_Direct Traffic                                float64
lead_source_Facebook                                      float64
lead_source_Google                                        float64
lead_source_Live Chat                                     float64
lead_source_NC_EDM                                        float64
lead_source_Olark Chat                                    float64
lead_source_Organic Search                                float64
lead_source_Pay per Click Ads                             float64
lead_source_Press_Release                                 float64
lead_source_Reference                                     float64
lead_source_Referral Sites                                float64
lead_source_Social Media                                  float64
lead_source_WeLearn                                       float64
lead_source_Welingak Website                              float64
lead_source_bing                                          float64
lead_source_blog                                          float64
lead_source_google                                        float64
lead_source_testone                                       float64
lead_source_welearnblog_Home                              float64
lead_source_youtubechannel                                float64
tags_Already a student                                    float64
tags_Busy                                                 float64
tags_Closed by Horizzon                                   float64
tags_Diploma holder (Not Eligible)                        float64
tags_Graduation in progress                               float64
tags_In confusion whether part time or DLP                float64
tags_Interested  in full time MBA                         float64
tags_Interested in Next batch                             float64
tags_Interested in other courses                          float64
tags_Lateral student                                      float64
tags_Lost to EINS                                         float64
tags_Lost to Others                                       float64
tags_Not doing further education                          float64
tags_Recognition issue (DEC approval)                     float64
tags_Ringing                                              float64
tags_Shall take in the next coming month                  float64
tags_Still Thinking                                       float64
tags_University not recognized                            float64
tags_Want to take admission but has financial problems    float64
tags_Will revert after reading the email                  float64
tags_in touch with EINS                                   float64
tags_invalid number                                       float64
tags_number not provided                                  float64
tags_opp hangup                                           float64
tags_switched off                                         float64
tags_wrong number given                                   float64
specialization_Banking, Investment And Insurance          float64
specialization_Business Administration                    float64
specialization_E-Business                                 float64
specialization_E-COMMERCE                                 float64
specialization_Finance Management                         float64
specialization_Healthcare Management                      float64
specialization_Hospitality Management                     float64
specialization_Human Resource Management                  float64
specialization_IT Projects Management                     float64
specialization_International Business                     float64
specialization_Marketing Management                       float64
specialization_Media and Advertising                      float64
specialization_Operations Management                      float64
specialization_Retail Management                          float64
specialization_Rural and Agribusiness                     float64
specialization_Services Excellence                        float64
specialization_Supply Chain Management                    float64
specialization_Travel and Tourism                         float64
dtype: object

# Post split we perform standard scaling they fit and transform the train data set

# to_scale = ['lead_number', 'totalvisits', 'ttime_on_site', 'pg_view_pv']
to_scale = X.columns
to_scale

scaler = MinMaxScaler()
X_train[to_scale] = scaler.fit_transform(X_train[to_scale], y_train)
X_train.head()

Index(['totalvisits', 'ttime_on_site', 'pg_view_pv', 'avail_free_copy_1',
       'city_Other Cities', 'city_Other Cities of Maharashtra',
       'city_Other Metro Cities', 'city_Thane & Outskirts',
       'city_Tier II Cities', 'curr_occupation_Housewife',
       'curr_occupation_Other', 'curr_occupation_Student',
       'curr_occupation_Unemployed', 'curr_occupation_Working Professional',
       'do_not_email_1', 'last_activity_Converted to Lead',
       'last_activity_Email Bounced', 'last_activity_Email Link Clicked',
       'last_activity_Email Marked Spam', 'last_activity_Email Opened',
       'last_activity_Email Received',
       'last_activity_Form Submitted on Website',
       'last_activity_Had a Phone Conversation',
       'last_activity_Olark Chat Conversation',
       'last_activity_Page Visited on Website',
       'last_activity_Resubscribed to emails', 'last_activity_SMS Sent',
       'last_activity_Unreachable', 'last_activity_Unsubscribed',
       'last_activity_View in browser link Clicked',
       'last_activity_Visited Booth in Tradeshow',
       'lead_origin_Landing Page Submission', 'lead_origin_Lead Add Form',
       'lead_origin_Lead Import', 'lead_origin_Quick Add Form',
       'lead_source_Direct Traffic', 'lead_source_Facebook',
       'lead_source_Google', 'lead_source_Live Chat', 'lead_source_NC_EDM',
       'lead_source_Olark Chat', 'lead_source_Organic Search',
       'lead_source_Pay per Click Ads', 'lead_source_Press_Release',
       'lead_source_Reference', 'lead_source_Referral Sites',
       'lead_source_Social Media', 'lead_source_WeLearn',
       'lead_source_Welingak Website', 'lead_source_bing', 'lead_source_blog',
       'lead_source_google', 'lead_source_testone',
       'lead_source_welearnblog_Home', 'lead_source_youtubechannel',
       'tags_Already a student', 'tags_Busy', 'tags_Closed by Horizzon',
       'tags_Diploma holder (Not Eligible)', 'tags_Graduation in progress',
       'tags_In confusion whether part time or DLP',
       'tags_Interested  in full time MBA', 'tags_Interested in Next batch',
       'tags_Interested in other courses', 'tags_Lateral student',
       'tags_Lost to EINS', 'tags_Lost to Others',
       'tags_Not doing further education',
       'tags_Recognition issue (DEC approval)', 'tags_Ringing',
       'tags_Shall take in the next coming month', 'tags_Still Thinking',
       'tags_University not recognized',
       'tags_Want to take admission but has financial problems',
       'tags_Will revert after reading the email', 'tags_in touch with EINS',
       'tags_invalid number', 'tags_number not provided', 'tags_opp hangup',
       'tags_switched off', 'tags_wrong number given',
       'specialization_Banking, Investment And Insurance',
       'specialization_Business Administration', 'specialization_E-Business',
       'specialization_E-COMMERCE', 'specialization_Finance Management',
       'specialization_Healthcare Management',
       'specialization_Hospitality Management',
       'specialization_Human Resource Management',
       'specialization_IT Projects Management',
       'specialization_International Business',
       'specialization_Marketing Management',
       'specialization_Media and Advertising',
       'specialization_Operations Management',
       'specialization_Retail Management',
       'specialization_Rural and Agribusiness',
       'specialization_Services Excellence',
       'specialization_Supply Chain Management',
       'specialization_Travel and Tourism'],
      dtype='object')

# We create custom functions for model veiling since iteration we reuse certain functions again and again
# Train and predict function trains the model and predicts on the same data and returns the model its probability and predicted values based on cutoff
# The matrix function returns confusion matrix and accuracy score
# The vif function returns the vif score for the features

def logreg_train_pred_fn(fX_train, fy_train, fcol, fcutoff):
    fX_train_sm = sm.add_constant(fX_train[fcol])
    flogm = sm.GLM(fy_train, fX_train_sm, family = sm.families.Binomial())
    fres = flogm.fit()
    fy_train_pred = fres.predict(fX_train_sm)
    fy_train_pred = fy_train_pred.values.reshape(-1)
    fy_train_pred_final = pd.DataFrame({'Converted':fy_train.values, 'Conv_Prob':fy_train_pred})
    fy_train_pred_final['ID'] = fy_train.index
    fy_train_pred_final['predicted'] = fy_train_pred_final.Conv_Prob.map(lambda x: 1 if x > fcutoff else 0)
    return fres, fy_train_pred,fy_train_pred_final

def logreg_metrics_fn(fy_train_pred_final):
    fconfusion = confusion_matrix(fy_train_pred_final.Converted, fy_train_pred_final.predicted )
    faccuracy = accuracy_score(fy_train_pred_final.Converted, fy_train_pred_final.predicted)
    return fconfusion, faccuracy
    
def logreg_VIF_score_fn(fX_train, fcol):
    fvif = pd.DataFrame()
    fvif['Features'] = fX_train[fcol].columns
    fvif['VIF'] = [variance_inflation_factor(fX_train[fcol].values, i) for i in range(fX_train[fcol].shape[1])]
    fvif['VIF'] = round(fvif['VIF'], 2)
    fvif = fvif.sort_values(by = "VIF", ascending = False)
    return fvif

# Logistic regression model
logm1 = sm.GLM(y_train,(sm.add_constant(X_train)), family = sm.families.Binomial())
res = logm1.fit()
# res.summary()

# Since the data set has a lot of features we perform rfe to eliminate insignificant features
logreg = LogisticRegression()

rfe = RFE(estimator=logreg, n_features_to_select=15)             # running RFE with 15 variables as output
rfe = rfe.fit(X_train, y_train)
rfe_feature_Ranking = list(zip(X_train.columns, rfe.support_, rfe.ranking_))

rfe_sorted = sorted(rfe_feature_Ranking, key=lambda x : x[2])
rfe_sorted[:15]

[('ttime_on_site', True, 1),
 ('last_activity_SMS Sent', True, 1),
 ('lead_source_Welingak Website', True, 1),
 ('tags_Already a student', True, 1),
 ('tags_Closed by Horizzon', True, 1),
 ('tags_Interested  in full time MBA', True, 1),
 ('tags_Interested in other courses', True, 1),
 ('tags_Lost to EINS', True, 1),
 ('tags_Not doing further education', True, 1),
 ('tags_Ringing', True, 1),
 ('tags_Will revert after reading the email', True, 1),
 ('tags_invalid number', True, 1),
 ('tags_number not provided', True, 1),
 ('tags_switched off', True, 1),
 ('tags_wrong number given', True, 1)]

col = X_train.columns[rfe.support_]
# X_train.columns[~rfe.support_]

# Now we perform model iteration as many times as possible till we get an optimum result

cutoff = 0.5
res, y_train_pred,y_train_pred_final = logreg_train_pred_fn(X_train, y_train, col, cutoff)
confusion, accuracy = logreg_metrics_fn(y_train_pred_final)
vif = logreg_VIF_score_fn(X_train, col)

print('Model Summary:')          # Model Summary:
res.summary()
print('\nVIF Score:')            # VIF Score:
vif

# print('\nY_Predicted Values:')   # Y_Predicted Values:
# y_train_pred
# print('\nY_Predicted Cutoff:')   # Y_Predicted Cutoff:
# y_train_pred_final
# print('\nConfusion Matrix:')     # Confusion Matrix: 
# confusion
# print(f'\nAccuracy Score: {accuracy}\n')       # Accuracy Score:

Model Summary:

VIF Score:

col = col.drop('tags_number not provided', 1)
col

cutoff = 0.5
res, y_train_pred,y_train_pred_final = logreg_train_pred_fn(X_train, y_train, col, cutoff)
confusion, accuracy = logreg_metrics_fn(y_train_pred_final)
vif = logreg_VIF_score_fn(X_train, col)

print('Model Summary:')          # Model Summary:
res.summary()
print('\nVIF Score:')            # VIF Score:
vif

# print('\nY_Predicted Values:')   # Y_Predicted Values:
# y_train_pred
# print('\nY_Predicted Cutoff:')   # Y_Predicted Cutoff:
# y_train_pred_final
# print('\nConfusion Matrix:')     # Confusion Matrix: 
# confusion
# print(f'\nAccuracy Score: {accuracy}\n')   # Accuracy Score:

Index(['ttime_on_site', 'last_activity_SMS Sent',
       'lead_source_Welingak Website', 'tags_Already a student',
       'tags_Closed by Horizzon', 'tags_Interested  in full time MBA',
       'tags_Interested in other courses', 'tags_Lost to EINS',
       'tags_Not doing further education', 'tags_Ringing',
       'tags_Will revert after reading the email', 'tags_invalid number',
       'tags_switched off', 'tags_wrong number given'],
      dtype='object')

Model Summary:

VIF Score:

col = col.drop('tags_wrong number given', 1)
col

cutoff = 0.5
res, y_train_pred,y_train_pred_final = logreg_train_pred_fn(X_train, y_train, col, cutoff)
confusion, accuracy = logreg_metrics_fn(y_train_pred_final)
vif = logreg_VIF_score_fn(X_train, col)

print('Model Summary:')          # Model Summary:
res.summary()
print('\nVIF Score:')            # VIF Score:
vif

# print('\nY_Predicted Values:')   # Y_Predicted Values:
# y_train_pred
# print('\nY_Predicted Cutoff:')   # Y_Predicted Cutoff:
# y_train_pred_final
print('\nConfusion Matrix:')     # Confusion Matrix: 
confusion
print(f'\nAccuracy Score: {accuracy}\n')   # Accuracy Score:

Index(['ttime_on_site', 'last_activity_SMS Sent',
       'lead_source_Welingak Website', 'tags_Already a student',
       'tags_Closed by Horizzon', 'tags_Interested  in full time MBA',
       'tags_Interested in other courses', 'tags_Lost to EINS',
       'tags_Not doing further education', 'tags_Ringing',
       'tags_Will revert after reading the email', 'tags_invalid number',
       'tags_switched off'],
      dtype='object')

Model Summary:

VIF Score:

Confusion Matrix:

array([[4334,  172],
       [ 487, 2289]])

Accuracy Score: 0.9095028838231255

TP = confusion[1,1] # true positive 
TN = confusion[0,0] # true negatives
FP = confusion[0,1] # false positives
FN = confusion[1,0] # false negatives

# Calculate the sensitivity
TP/(TP+FN)

# Calculate the specificity
TN/(TN+FP)

0.8245677233429395

0.9618286728806036

# Let's create columns with different probability cutoffs 
numbers = [float(x)/10 for x in range(10)]
for i in numbers:
    y_train_pred_final[i]= y_train_pred_final.Conv_Prob.map(lambda x: 1 if x > i else 0)
y_train_pred_final.head()

# Now let's calculate accuracy sensitivity and specificity for various probability cutoffs.
cutoff_df = pd.DataFrame( columns = ['prob','accuracy','sensi','speci'])

num = [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]
for i in num:
    cm1 = confusion_matrix(y_train_pred_final.Converted, y_train_pred_final[i] )
    total1=sum(sum(cm1))
    accuracy = (cm1[0,0]+cm1[1,1])/total1
    
    speci = cm1[0,0]/(cm1[0,0]+cm1[0,1])
    sensi = cm1[1,1]/(cm1[1,0]+cm1[1,1])
    cutoff_df.loc[i] =[ i ,accuracy,sensi,speci]
print(cutoff_df)

       prob  accuracy  sensi  speci
0.000 0.000     0.381  1.000  0.000
0.100 0.100     0.827  0.953  0.750
0.200 0.200     0.891  0.936  0.863
0.300 0.300     0.896  0.914  0.884
0.400 0.400     0.900  0.846  0.933
0.500 0.500     0.910  0.825  0.962
0.600 0.600     0.910  0.811  0.970
0.700 0.700     0.905  0.792  0.974
0.800 0.800     0.896  0.754  0.983
0.900 0.900     0.868  0.670  0.990

# Let's plot accuracy sensitivity and specificity for various probabilities.
cutoff_df.plot.line(x='prob', y=['accuracy','sensi','speci'])
plt.show()

<Axes: xlabel='prob'>

y_train_pred_final['final_predicted'] = y_train_pred_final.Conv_Prob.map( lambda x: 1 if x > 0.34 else 0)
y_train_pred_final.head()

# Let's check the overall accuracy.
accuracy_score(y_train_pred_final.Converted, y_train_pred_final.final_predicted)

0.8893161219445207

confusion = confusion_matrix(y_train_pred_final.Converted, y_train_pred_final.final_predicted )
confusion

array([[4061,  445],
       [ 361, 2415]])

TP = confusion[1,1] # true positive 
TN = confusion[0,0] # true negatives
FP = confusion[0,1] # false positives
FN = confusion[1,0] # false negatives

# Calculate the sensitivity
TP/(TP+FN)

# Calculate the specificity
TN/(TN+FP)

0.8699567723342939

0.901242787394585

def draw_roc( actual, probs ):
    fpr, tpr, thresholds = roc_curve( actual, probs, drop_intermediate = False )
    auc_score = roc_auc_score( actual, probs )
    plt.figure(figsize=(5, 5))
    plt.plot( fpr, tpr, label='ROC curve (area = %0.2f)' % auc_score )
    plt.plot([0, 1], [0, 1], 'k--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate or [1 - True Negative Rate]')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver operating characteristic')
    plt.legend(loc="lower right")
    plt.show()
    return None

fpr, tpr, thresholds = roc_curve( y_train_pred_final.Converted, y_train_pred_final.Conv_Prob, drop_intermediate = False )
draw_roc(y_train_pred_final.Converted, y_train_pred_final.Conv_Prob)

precision_score(y_train_pred_final.Converted, y_train_pred_final.predicted)
recall_score(y_train_pred_final.Converted, y_train_pred_final.predicted)

0.9301097114993905

0.8245677233429395

p, r, thresholds = precision_recall_curve(y_train_pred_final.Converted, y_train_pred_final.Conv_Prob)
plt.plot(thresholds, p[:-1], "b")
plt.plot(thresholds, r[:-1], "r")
plt.title('Precision Recall Curve')
plt.show();

y_train_pred_final['final_predicted'] = y_train_pred_final.Conv_Prob.map( lambda x: 1 if x > 0.37 else 0)
y_train_pred_final.head()

# Let's check the overall accuracy.
accuracy_score(y_train_pred_final.Converted, y_train_pred_final.final_predicted)

0.8950837681955507

confusion = confusion_matrix(y_train_pred_final.Converted, y_train_pred_final.final_predicted )
confusion

array([[4141,  365],
       [ 399, 2377]])

TP = confusion[1,1] # true positive 
TN = confusion[0,0] # true negatives
FP = confusion[0,1] # false positives
FN = confusion[1,0] # false negatives

# Calculate the sensitivity
TP/(TP+FN)

# Calculate the specificity
TN/(TN+FP)

precision_score(y_train_pred_final.Converted, y_train_pred_final.predicted)
recall_score(y_train_pred_final.Converted, y_train_pred_final.predicted)

0.8562680115273775

0.9189968930315136

0.9301097114993905

0.8245677233429395

def logreg_test_pred_fn(fX_test, fy_test, fcol, fcutoff, fres):
    fX_test_sm = sm.add_constant(fX_test[fcol])
    fy_test_pred = fres.predict(fX_test_sm)
    fy_test_pred = fy_test_pred.values.reshape(-1)
    fy_test_pred_final = pd.DataFrame({'Converted':fy_test.values, 'Conv_Prob':fy_test_pred})
    fy_test_pred_final['ID'] = fy_test.index
    fy_test_pred_final['predicted'] = fy_test_pred_final.Conv_Prob.map(lambda x: 1 if x > fcutoff else 0)
    return fres, fy_test_pred,fy_test_pred_final

def logreg_test_metrics_fn(fy_test_pred_final):
    fconfusion = confusion_matrix(fy_test_pred_final.Converted, fy_test_pred_final.predicted )
    faccuracy = accuracy_score(fy_test_pred_final.Converted, fy_test_pred_final.predicted)
    return fconfusion, faccuracy
    
def logreg_test_VIF_score_fn(fX_test, fcol):
    fvif = pd.DataFrame()
    fvif['Features'] = fX_test[fcol].columns
    fvif['VIF'] = [variance_inflation_factor(fX_test[fcol].values, i) for i in 
                   range(fX_test[fcol].shape[1])]
    fvif['VIF'] = round(fvif['VIF'], 2)
    fvif = fvif.sort_values(by = "VIF", ascending = False)
    return fvif

X_test[to_scale] = scaler.transform(X_test[to_scale])
X_test[col].head()

cutoff = 0.37
res, y_test_pred, y_test_pred_final = logreg_test_pred_fn(X_test, y_test, col, cutoff, res)
confusion, accuracy = logreg_test_metrics_fn(y_test_pred_final)
vif = logreg_test_VIF_score_fn(X_test, col)

print('Model Summary:')          # Model Summary:
res.summary()
print('\nVIF Score:')            # VIF Score:
vif

# print('\nY_Predicted Values:')   # Y_Predicted Values:
# y_test_pred
# print('\nY_Predicted Cutoff:')   # Y_Predicted Cutoff:
# y_test_pred_final

Model Summary:

VIF Score:

print('\nConfusion Matrix:')     # Confusion Matrix: 
confusion
print(f'\nAccuracy Score: {accuracy}\n')  # Accuracy Score:

Confusion Matrix:

array([[1045,  100],
       [  76,  600]])

Accuracy Score: 0.9033498077979132

TP = confusion[1,1] # true positive 
TN = confusion[0,0] # true negatives
FP = confusion[0,1] # false positives
FN = confusion[1,0] # false negatives

# Calculate the sensitivity
TP/(TP+FN)

# Calculate the specificity
TN/(TN+FP)

precision_score(y_train_pred_final.Converted, y_train_pred_final.predicted)
recall_score(y_train_pred_final.Converted, y_train_pred_final.predicted)

0.8875739644970414

0.9126637554585153

0.9301097114993905

0.8245677233429395

print(f'\nAccuracy Score: {accuracy}\n')  # Accuracy Score:

Accuracy Score: 0.9033498077979132

logreg = LogisticRegression()
res = logreg.fit(X_train[col],y_train)

scores = cross_val_score(res, X_train[col], y_train, cv=5, scoring='accuracy')
np.sqrt(np.abs(scores))
scores = cross_val_score(res, X_test[col], y_test, cv=5, scoring='accuracy')
np.sqrt(np.abs(scores))

array([0.95398597, 0.96258061, 0.94563776, 0.95790522, 0.94962399])

array([0.97080675, 0.96076892, 0.96076892, 0.96504854, 0.95070824])

	totalvisits	ttime_on_site	pg_view_pv	avail_free_copy_1	city_Other Cities	city_Other Cities of Maharashtra	city_Other Metro Cities	city_Thane & Outskirts	city_Tier II Cities	curr_occupation_Housewife	curr_occupation_Other	curr_occupation_Student	curr_occupation_Unemployed	curr_occupation_Working Professional	do_not_email_1	last_activity_Converted to Lead	last_activity_Email Bounced	last_activity_Email Link Clicked	last_activity_Email Marked Spam	last_activity_Email Opened	last_activity_Email Received	last_activity_Form Submitted on Website	last_activity_Had a Phone Conversation	last_activity_Olark Chat Conversation	last_activity_Page Visited on Website	last_activity_Resubscribed to emails	last_activity_SMS Sent	last_activity_Unreachable	last_activity_Unsubscribed	last_activity_View in browser link Clicked	last_activity_Visited Booth in Tradeshow	lead_origin_Landing Page Submission	lead_origin_Lead Add Form	lead_origin_Lead Import	lead_source_Direct Traffic	lead_source_Facebook	lead_source_Google	lead_source_Live Chat	lead_source_NC_EDM	lead_source_Olark Chat	lead_source_Organic Search	lead_source_Pay per Click Ads	lead_source_Press_Release	lead_source_Reference	lead_source_Referral Sites	lead_source_Social Media	lead_source_WeLearn	lead_source_Welingak Website	lead_source_bing	lead_source_blog	lead_source_google	lead_source_testone	lead_source_welearnblog_Home	lead_source_youtubechannel	tags_Already a student	tags_Busy	tags_Closed by Horizzon	tags_Diploma holder (Not Eligible)	tags_Graduation in progress	tags_In confusion whether part time or DLP	tags_Interested in full time MBA	tags_Interested in Next batch	tags_Interested in other courses	tags_Lateral student	tags_Lost to EINS	tags_Lost to Others	tags_Not doing further education	tags_Recognition issue (DEC approval)	tags_Ringing	tags_Shall take in the next coming month	tags_Still Thinking	tags_University not recognized	tags_Want to take admission but has financial problems	tags_Will revert after reading the email	tags_in touch with EINS	tags_invalid number	tags_number not provided	tags_opp hangup	tags_switched off	tags_wrong number given	specialization_Banking, Investment And Insurance	specialization_Business Administration	specialization_E-Business	specialization_E-COMMERCE	specialization_Finance Management	specialization_Healthcare Management	specialization_Hospitality Management	specialization_Human Resource Management	specialization_IT Projects Management	specialization_International Business	specialization_Marketing Management	specialization_Media and Advertising	specialization_Operations Management	specialization_Retail Management	specialization_Rural and Agribusiness	specialization_Services Excellence	specialization_Supply Chain Management	specialization_Travel and Tourism
7740	-0.779	-0.376	-0.712	1.471	-0.284	4.347	-0.208	-0.293	-0.086	-0.029	-0.042	-0.152	0.335	-0.284	-0.290	4.477	-0.192	-0.176	-0.017	-0.781	-0.017	-0.122	-0.054	-0.345	-0.275	-0.012	-0.647	-0.101	-0.078	-0.029	-0.012	0.925	-0.265	-0.063	1.619	-0.064	-0.687	-0.017	-0.012	-0.485	-0.381	-0.012	-0.017	-0.231	-0.120	-0.017	-0.012	-0.119	-0.017	-0.012	-0.023	-0.012	-0.012	-0.012	4.372	-0.141	-0.195	-0.086	-0.114	-0.023	-0.112	-0.020	-0.245	-0.020	-0.138	-0.029	-0.127	-0.012	-0.389	-0.012	-0.023	-0.017	-0.020	-0.532	-0.035	-0.091	-0.050	-0.062	-0.164	-0.072	-0.198	-0.214	-0.079	-0.113	-0.345	-0.133	-0.111	3.113	-0.208	-0.140	-0.318	-0.150	-0.232	-0.105	-0.088	-0.063	-0.199	-0.150
7014	0.971	0.881	-0.150	-0.680	-0.284	-0.230	-0.208	-0.293	-0.086	-0.029	-0.042	-0.152	0.335	-0.284	-0.290	-0.223	-0.192	-0.176	-0.017	1.280	-0.017	-0.122	-0.054	-0.345	-0.275	-0.012	-0.647	-0.101	-0.078	-0.029	-0.012	0.925	-0.265	-0.063	-0.618	-0.064	1.456	-0.017	-0.012	-0.485	-0.381	-0.012	-0.017	-0.231	-0.120	-0.017	-0.012	-0.119	-0.017	-0.012	-0.023	-0.012	-0.012	-0.012	-0.229	-0.141	-0.195	-0.086	-0.114	-0.023	-0.112	-0.020	-0.245	-0.020	-0.138	-0.029	-0.127	-0.012	-0.389	-0.012	-0.023	-0.017	-0.020	1.881	-0.035	-0.091	-0.050	-0.062	-0.164	-0.072	-0.198	-0.214	-0.079	-0.113	2.900	-0.133	-0.111	-0.321	-0.208	-0.140	-0.318	-0.150	-0.232	-0.105	-0.088	-0.063	-0.199	-0.150
2431	-0.429	-0.337	-0.150	-0.680	-0.284	-0.230	-0.208	-0.293	-0.086	-0.029	-0.042	-0.152	0.335	-0.284	-0.290	-0.223	-0.192	-0.176	-0.017	-0.781	-0.017	8.189	-0.054	-0.345	-0.275	-0.012	-0.647	-0.101	-0.078	-0.029	-0.012	0.925	-0.265	-0.063	-0.618	-0.064	1.456	-0.017	-0.012	-0.485	-0.381	-0.012	-0.017	-0.231	-0.120	-0.017	-0.012	-0.119	-0.017	-0.012	-0.023	-0.012	-0.012	-0.012	-0.229	-0.141	-0.195	-0.086	-0.114	-0.023	-0.112	-0.020	-0.245	-0.020	-0.138	-0.029	7.894	-0.012	-0.389	-0.012	-0.023	-0.017	-0.020	-0.532	-0.035	-0.091	-0.050	-0.062	-0.164	-0.072	-0.198	-0.214	-0.079	-0.113	-0.345	-0.133	-0.111	-0.321	4.808	-0.140	-0.318	-0.150	-0.232	-0.105	-0.088	-0.063	-0.199	-0.150
3012	1.672	1.539	0.973	-0.680	-0.284	-0.230	-0.208	-0.293	-0.086	-0.029	-0.042	-0.152	0.335	-0.284	-0.290	-0.223	-0.192	-0.176	-0.017	1.280	-0.017	-0.122	-0.054	-0.345	-0.275	-0.012	-0.647	-0.101	-0.078	-0.029	-0.012	-1.081	-0.265	-0.063	-0.618	-0.064	-0.687	-0.017	-0.012	-0.485	2.623	-0.012	-0.017	-0.231	-0.120	-0.017	-0.012	-0.119	-0.017	-0.012	-0.023	-0.012	-0.012	-0.012	-0.229	-0.141	-0.195	-0.086	-0.114	-0.023	-0.112	-0.020	-0.245	-0.020	-0.138	-0.029	-0.127	-0.012	-0.389	-0.012	-0.023	-0.017	-0.020	1.881	-0.035	-0.091	-0.050	-0.062	-0.164	-0.072	-0.198	-0.214	-0.079	-0.113	-0.345	7.506	-0.111	-0.321	-0.208	-0.140	-0.318	-0.150	-0.232	-0.105	-0.088	-0.063	-0.199	-0.150
3140	-1.130	-0.889	-1.274	-0.680	-0.284	-0.230	-0.208	-0.293	-0.086	-0.029	-0.042	-0.152	0.335	-0.284	-0.290	-0.223	-0.192	-0.176	-0.017	1.280	-0.017	-0.122	-0.054	-0.345	-0.275	-0.012	-0.647	-0.101	-0.078	-0.029	-0.012	-1.081	-0.265	-0.063	-0.618	-0.064	-0.687	-0.017	-0.012	2.062	-0.381	-0.012	-0.017	-0.231	-0.120	-0.017	-0.012	-0.119	-0.017	-0.012	-0.023	-0.012	-0.012	-0.012	-0.229	-0.141	-0.195	-0.086	-0.114	-0.023	-0.112	-0.020	-0.245	-0.020	-0.138	-0.029	-0.127	-0.012	-0.389	-0.012	-0.023	-0.017	-0.020	-0.532	-0.035	-0.091	-0.050	-0.062	-0.164	-0.072	-0.198	-0.214	-0.079	-0.113	-0.345	-0.133	-0.111	-0.321	-0.208	-0.140	-0.318	-0.150	-0.232	-0.105	-0.088	-0.063	-0.199	-0.150

Dep. Variable:	converted	No. Observations:	7282
Model:	GLM	Df Residuals:	7183
Model Family:	Binomial	Df Model:	98
Link Function:	Logit	Scale:	1.0000
Method:	IRLS	Log-Likelihood:	nan
Date:	Sun, 20 Oct 2024	Deviance:	69906.
Time:	23:04:16	Pearson chi2:	3.42e+18
No. Iterations:	100	Pseudo R-squ. (CS):	nan
Covariance Type:	nonrobust

	coef	std err	z	P>\|z\|	[0.025	0.975]
const	-2.981e+14	7.86e+05	-3.79e+08	0.000	-2.98e+14	-2.98e+14
totalvisits	1.437e+14	1.3e+06	1.11e+08	0.000	1.44e+14	1.44e+14
ttime_on_site	9.087e+13	9.56e+05	9.51e+07	0.000	9.09e+13	9.09e+13
pg_view_pv	-1.353e+14	1.44e+06	-9.37e+07	0.000	-1.35e+14	-1.35e+14
avail_free_copy_1	-5.638e+13	1.18e+06	-4.8e+07	0.000	-5.64e+13	-5.64e+13
city_Other Cities	1.495e+13	8.39e+05	1.78e+07	0.000	1.49e+13	1.49e+13
city_Other Cities of Maharashtra	6.152e+12	8.29e+05	7.42e+06	0.000	6.15e+12	6.15e+12
city_Other Metro Cities	6.251e+12	8.28e+05	7.55e+06	0.000	6.25e+12	6.25e+12
city_Thane & Outskirts	-8.958e+12	8.34e+05	-1.07e+07	0.000	-8.96e+12	-8.96e+12
city_Tier II Cities	2.437e+13	8e+05	3.05e+07	0.000	2.44e+13	2.44e+13
curr_occupation_Housewife	1.005e+14	1.12e+06	8.99e+07	0.000	1.01e+14	1.01e+14
curr_occupation_Other	-5.502e+11	1.41e+06	-3.91e+05	0.000	-5.5e+11	-5.5e+11
curr_occupation_Student	4.004e+13	4.18e+06	9.58e+06	0.000	4e+13	4e+13
curr_occupation_Unemployed	6.388e+13	8.31e+06	7.69e+06	0.000	6.39e+13	6.39e+13
curr_occupation_Working Professional	9.028e+13	7.3e+06	1.24e+07	0.000	9.03e+13	9.03e+13
do_not_email_1	-1.029e+14	1.13e+06	-9.07e+07	0.000	-1.03e+14	-1.03e+14
last_activity_Converted to Lead	-3.893e+14	5.96e+06	-6.54e+07	0.000	-3.89e+14	-3.89e+14
last_activity_Email Bounced	-3.002e+14	5.25e+06	-5.72e+07	0.000	-3e+14	-3e+14
last_activity_Email Link Clicked	-2.886e+14	4.78e+06	-6.04e+07	0.000	-2.89e+14	-2.89e+14
last_activity_Email Marked Spam	2.959e+13	9.15e+05	3.23e+07	0.000	2.96e+13	2.96e+13
last_activity_Email Opened	-7.692e+14	1.34e+07	-5.72e+07	0.000	-7.69e+14	-7.69e+14
last_activity_Email Received	-1.686e+13	9.13e+05	-1.85e+07	0.000	-1.69e+13	-1.69e+13
last_activity_Form Submitted on Website	-1.924e+14	3.43e+06	-5.61e+07	0.000	-1.92e+14	-1.92e+14
last_activity_Had a Phone Conversation	-1.307e+14	1.68e+06	-7.77e+07	0.000	-1.31e+14	-1.31e+14
last_activity_Olark Chat Conversation	-5.975e+14	8.57e+06	-6.97e+07	0.000	-5.98e+14	-5.98e+14
last_activity_Page Visited on Website	-4.675e+14	7.12e+06	-6.56e+07	0.000	-4.67e+14	-4.67e+14
last_activity_Resubscribed to emails	1.59e+13	8.54e+05	1.86e+07	0.000	1.59e+13	1.59e+13
last_activity_SMS Sent	-6.373e+14	1.27e+07	-5.04e+07	0.000	-6.37e+14	-6.37e+14
last_activity_Unreachable	-1.537e+14	2.87e+06	-5.36e+07	0.000	-1.54e+14	-1.54e+14
last_activity_Unsubscribed	-1.057e+14	2.31e+06	-4.57e+07	0.000	-1.06e+14	-1.06e+14
last_activity_View in browser link Clicked	-5.007e+13	1.12e+06	-4.47e+07	0.000	-5.01e+13	-5.01e+13
last_activity_Visited Booth in Tradeshow	-1.672e+13	8.62e+05	-1.94e+07	0.000	-1.67e+13	-1.67e+13
lead_origin_Landing Page Submission	-4.99e+13	1.6e+06	-3.12e+07	0.000	-4.99e+13	-4.99e+13
lead_origin_Lead Add Form	1.512e+14	9.63e+06	1.57e+07	0.000	1.51e+14	1.51e+14
lead_origin_Lead Import	2.958e+14	4.34e+06	6.82e+07	0.000	2.96e+14	2.96e+14
lead_origin_Quick Add Form	-6.8567	1.14e-07	-5.99e+07	0.000	-6.857	-6.857
lead_source_Direct Traffic	-1.521e+15	2.75e+07	-5.54e+07	0.000	-1.52e+15	-1.52e+15
lead_source_Facebook	-5.653e+14	5.86e+06	-9.65e+07	0.000	-5.65e+14	-5.65e+14
lead_source_Google	-1.609e+15	2.87e+07	-5.62e+07	0.000	-1.61e+15	-1.61e+15
lead_source_Live Chat	-2.153e+13	1.12e+06	-1.93e+07	0.000	-2.15e+13	-2.15e+13
lead_source_NC_EDM	1.646e+13	1.07e+06	1.54e+07	0.000	1.65e+13	1.65e+13
lead_source_Olark Chat	-1.224e+15	2.41e+07	-5.08e+07	0.000	-1.22e+15	-1.22e+15
lead_source_Organic Search	-1.105e+15	2.05e+07	-5.4e+07	0.000	-1.1e+15	-1.1e+15
lead_source_Pay per Click Ads	-7.3e+13	1.07e+06	-6.84e+07	0.000	-7.3e+13	-7.3e+13
lead_source_Press_Release	-9.626e+13	1.29e+06	-7.46e+07	0.000	-9.63e+13	-9.63e+13
lead_source_Reference	-9.203e+14	1.05e+07	-8.77e+07	0.000	-9.2e+14	-9.2e+14
lead_source_Referral Sites	-4.156e+14	7.33e+06	-5.67e+07	0.000	-4.16e+14	-4.16e+14
lead_source_Social Media	-4.731e+13	1.29e+06	-3.67e+07	0.000	-4.73e+13	-4.73e+13
lead_source_WeLearn	-5.926e+12	1.07e+06	-5.55e+06	0.000	-5.93e+12	-5.93e+12
lead_source_Welingak Website	-3.289e+14	5.63e+06	-5.84e+07	0.000	-3.29e+14	-3.29e+14
lead_source_bing	-1.19e+14	1.29e+06	-9.25e+07	0.000	-1.19e+14	-1.19e+14
lead_source_blog	-8.328e+13	1.07e+06	-7.8e+07	0.000	-8.33e+13	-8.33e+13
lead_source_google	-1.69e+14	1.64e+06	-1.03e+08	0.000	-1.69e+14	-1.69e+14
lead_source_testone	-7.475e+13	1.07e+06	-6.99e+07	0.000	-7.48e+13	-7.48e+13
lead_source_welearnblog_Home	-8.384e+13	1.07e+06	-7.85e+07	0.000	-8.38e+13	-8.38e+13
lead_source_youtubechannel	-8.404e+13	1.07e+06	-7.83e+07	0.000	-8.4e+13	-8.4e+13
tags_Already a student	-1.872e+14	8.69e+05	-2.16e+08	0.000	-1.87e+14	-1.87e+14
tags_Busy	1.782e+14	8.18e+05	2.18e+08	0.000	1.78e+14	1.78e+14
tags_Closed by Horizzon	3.694e+14	9.07e+05	4.07e+08	0.000	3.69e+14	3.69e+14
tags_Diploma holder (Not Eligible)	-8.49e+13	7.97e+05	-1.07e+08	0.000	-8.49e+13	-8.49e+13
tags_Graduation in progress	-2.813e+13	8.04e+05	-3.5e+07	0.000	-2.81e+13	-2.81e+13
tags_In confusion whether part time or DLP	-9.315e+13	7.88e+05	-1.18e+08	0.000	-9.31e+13	-9.31e+13
tags_Interested in full time MBA	-6.773e+13	8.03e+05	-8.44e+07	0.000	-6.77e+13	-6.77e+13
tags_Interested in Next batch	9.869e+13	7.9e+05	1.25e+08	0.000	9.87e+13	9.87e+13
tags_Interested in other courses	-1.556e+14	8.41e+05	-1.85e+08	0.000	-1.56e+14	-1.56e+14
tags_Lateral student	9.589e+13	7.88e+05	1.22e+08	0.000	9.59e+13	9.59e+13
tags_Lost to EINS	2.478e+14	8.17e+05	3.03e+08	0.000	2.48e+14	2.48e+14
tags_Lost to Others	-1.099e+14	7.91e+05	-1.39e+08	0.000	-1.1e+14	-1.1e+14
tags_Not doing further education	-7.189e+13	8.23e+05	-8.74e+07	0.000	-7.19e+13	-7.19e+13
tags_Recognition issue (DEC approval)	-4.783e+13	8.01e+05	-5.97e+07	0.000	-4.78e+13	-4.78e+13
tags_Ringing	-2.414e+14	8.93e+05	-2.7e+08	0.000	-2.41e+14	-2.41e+14
tags_Shall take in the next coming month	-4.423e+13	7.88e+05	-5.62e+07	0.000	-4.42e+13	-4.42e+13
tags_Still Thinking	-1.156e+13	7.92e+05	-1.46e+07	0.000	-1.16e+13	-1.16e+13
tags_University not recognized	-6.546e+13	7.9e+05	-8.28e+07	0.000	-6.55e+13	-6.55e+13
tags_Want to take admission but has financial problems	1.814e+13	7.96e+05	2.28e+07	0.000	1.81e+13	1.81e+13
tags_Will revert after reading the email	8.643e+14	1.09e+06	7.94e+08	0.000	8.64e+14	8.64e+14
tags_in touch with EINS	5.208e+12	7.9e+05	6.59e+06	0.000	5.21e+12	5.21e+12
tags_invalid number	-6.92e+13	7.99e+05	-8.66e+07	0.000	-6.92e+13	-6.92e+13
tags_number not provided	-8.165e+13	7.93e+05	-1.03e+08	0.000	-8.16e+13	-8.16e+13
tags_opp hangup	-7.313e+13	7.93e+05	-9.23e+07	0.000	-7.31e+13	-7.31e+13
tags_switched off	-1.147e+14	8.17e+05	-1.41e+08	0.000	-1.15e+14	-1.15e+14
tags_wrong number given	-2.666e+14	8.07e+05	-3.3e+08	0.000	-2.67e+14	-2.67e+14
specialization_Banking, Investment And Insurance	-4.518e+13	9.57e+05	-4.72e+07	0.000	-4.52e+13	-4.52e+13
specialization_Business Administration	-1.49e+13	9.89e+05	-1.51e+07	0.000	-1.49e+13	-1.49e+13
specialization_E-Business	-2.845e+12	8.23e+05	-3.46e+06	0.000	-2.85e+12	-2.85e+12
specialization_E-COMMERCE	-2.558e+13	8.57e+05	-2.99e+07	0.000	-2.56e+13	-2.56e+13
specialization_Finance Management	-5.568e+13	1.18e+06	-4.73e+07	0.000	-5.57e+13	-5.57e+13
specialization_Healthcare Management	-2.026e+13	8.77e+05	-2.31e+07	0.000	-2.03e+13	-2.03e+13
specialization_Hospitality Management	-2.688e+13	8.45e+05	-3.18e+07	0.000	-2.69e+13	-2.69e+13
specialization_Human Resource Management	-6.01e+13	1.13e+06	-5.3e+07	0.000	-6.01e+13	-6.01e+13
specialization_IT Projects Management	-1.731e+13	9.86e+05	-1.76e+07	0.000	-1.73e+13	-1.73e+13
specialization_International Business	-4.174e+13	8.82e+05	-4.73e+07	0.000	-4.17e+13	-4.17e+13
specialization_Marketing Management	-2.795e+13	1.11e+06	-2.53e+07	0.000	-2.79e+13	-2.79e+13
specialization_Media and Advertising	-4.26e+13	9.01e+05	-4.73e+07	0.000	-4.26e+13	-4.26e+13
specialization_Operations Management	-4.901e+13	1.01e+06	-4.86e+07	0.000	-4.9e+13	-4.9e+13
specialization_Retail Management	-4.408e+13	8.47e+05	-5.2e+07	0.000	-4.41e+13	-4.41e+13
specialization_Rural and Agribusiness	-9.661e+12	8.34e+05	-1.16e+07	0.000	-9.66e+12	-9.66e+12
specialization_Services Excellence	-1.614e+13	8.16e+05	-1.98e+07	0.000	-1.61e+13	-1.61e+13
specialization_Supply Chain Management	-4.553e+13	9.65e+05	-4.72e+07	0.000	-4.55e+13	-4.55e+13
specialization_Travel and Tourism	-7.602e+13	9.16e+05	-8.3e+07	0.000	-7.6e+13	-7.6e+13

Dep. Variable:	converted	No. Observations:	7282
Model:	GLM	Df Residuals:	7266
Model Family:	Binomial	Df Model:	15
Link Function:	Logit	Scale:	1.0000
Method:	IRLS	Log-Likelihood:	-1601.1
Date:	Sun, 20 Oct 2024	Deviance:	3202.1
Time:	23:04:22	Pearson chi2:	8.54e+03
No. Iterations:	22	Pseudo R-squ. (CS):	0.5892
Covariance Type:	nonrobust

	coef	std err	z	P>\|z\|	[0.025	0.975]
const	-0.8841	60.489	-0.015	0.988	-119.440	117.672
ttime_on_site	1.0554	0.052	20.110	0.000	0.953	1.158
last_activity_Olark Chat Conversation	-0.4699	0.064	-7.347	0.000	-0.595	-0.345
last_activity_SMS Sent	0.8580	0.046	18.699	0.000	0.768	0.948
lead_origin_Lead Add Form	0.4000	0.091	4.405	0.000	0.222	0.578
lead_source_Olark Chat	0.5871	0.051	11.412	0.000	0.486	0.688
lead_source_Welingak Website	0.5735	0.126	4.555	0.000	0.327	0.820
tags_Already a student	-0.7594	0.156	-4.853	0.000	-1.066	-0.453
tags_Closed by Horizzon	1.2012	0.136	8.801	0.000	0.934	1.469
tags_Interested in other courses	-0.4510	0.074	-6.087	0.000	-0.596	-0.306
tags_Lost to EINS	0.6964	0.071	9.754	0.000	0.556	0.836
tags_Not doing further education	-0.3755	0.126	-2.972	0.003	-0.623	-0.128
tags_Ringing	-0.9581	0.070	-13.702	0.000	-1.095	-0.821
tags_Will revert after reading the email	1.9035	0.069	27.620	0.000	1.768	2.039
tags_switched off	-0.4885	0.083	-5.851	0.000	-0.652	-0.325
tags_wrong number given	-1.6314	835.166	-0.002	0.998	-1638.527	1635.264

	Desc	Var	Value	Perc
0	Constant	magazine	No	100.000
1	Constant	more_course_updates	No	100.000
2	Constant	supply_chain_info	No	100.000
3	Constant	get_dm	No	100.000
4	Quasi Constant	x_education_forums	No	99.989
5	Quasi Constant	newspaper	No	99.989
6	Quasi Constant	do_not_call	No	99.978
7	Quasi Constant	newspaper_article	No	99.978
8	Quasi Constant	digital_advertisement	No	99.957
9	Quasi Constant	through_recommendations	No	99.924

	converted	totalvisits	ttime_on_site	pg_view_pv
count	9240.000	9103.000	9240.000	9103.000
mean	0.385	3.445	487.698	2.363
std	0.487	4.855	548.021	2.161
min	0.000	0.000	0.000	0.000
25%	0.000	1.000	12.000	1.000
50%	0.000	3.000	248.000	2.000
75%	1.000	5.000	936.000	3.000
max	1.000	251.000	2272.000	55.000

	lead_origin	lead_source	do_not_email	last_activity	country	specialization	curr_occupation	tags	city	avail_free_copy
count	9240	9204	9240	9137	6779	9240	6550	9240	5571	9240
unique	5	21	2	17	38	19	6	27	6	2
top	Landing Page Submission	Google	No	Email Opened	India	unknown	Unemployed	unknown	Mumbai	No
freq	4886	2868	8506	3437	6492	3380	5600	3353	3222	6352

	thresh_low	thresh_high
name
converted	-1.500	2.500
totalvisits	-5.000	11.000
ttime_on_site	-1374.000	2322.000
pg_view_pv	-2.000	6.000

	Features	VIF
3	lead_origin_Lead Add Form	1.670
4	lead_source_Olark Chat	1.450
12	tags_Will revert after reading the email	1.450
0	ttime_on_site	1.420
5	lead_source_Welingak Website	1.310
1	last_activity_Olark Chat Conversation	1.280
7	tags_Closed by Horizzon	1.220
11	tags_Ringing	1.180
2	last_activity_SMS Sent	1.170
8	tags_Interested in other courses	1.090
6	tags_Already a student	1.070
13	tags_switched off	1.050
9	tags_Lost to EINS	1.040
10	tags_Not doing further education	1.030
14	tags_wrong number given	1.010

	Converted	Conv_Prob	ID	predicted	0.000	0.100	0.200	0.300	0.400	0.500	0.600	0.700	0.800	0.900
0	0	0.002	7740	0	1	0	0	0	0	0	0	0	0	0
1	1	0.966	7014	1	1	1	1	1	1	1	1	1	1	1
2	0	0.004	2431	0	1	0	0	0	0	0	0	0	0	0
3	1	0.983	3012	1	1	1	1	1	1	1	1	1	1	1
4	0	0.165	3140	0	1	1	0	0	0	0	0	0	0	0

	ttime_on_site	last_activity_Olark Chat Conversation	last_activity_SMS Sent	lead_origin_Lead Add Form	lead_source_Olark Chat	lead_source_Welingak Website	tags_Already a student	tags_Closed by Horizzon	tags_Interested in other courses	tags_Lost to EINS	tags_Not doing further education	tags_Ringing	tags_Will revert after reading the email	tags_switched off
7421	-0.502	-0.345	-0.647	-0.265	-0.485	-0.119	-0.229	5.116	-0.245	-0.138	-0.127	-0.389	-0.532	-0.164
4570	-0.409	-0.345	1.545	-0.265	-0.485	-0.119	-0.229	-0.195	-0.245	-0.138	-0.127	-0.389	-0.532	-0.164

	lead_origin	lead_source	totalvisits	ttime_on_site	pg_view_pv	last_activity	specialization	curr_occupation	tags	city	avail_free_copy
7740	1	-1.063	-0.779	-0.376	-0.712	-1.823	7	-0.122	0	2	1
7014	1	-0.392	0.971	0.881	-0.150	-0.729	4	-0.122	19	0	0
2431	1	-0.392	-0.429	-0.337	-0.150	-0.182	8	-0.122	12	0	0
3012	0	0.949	1.672	1.539	0.973	-0.729	5	-0.122	19	0	0
3140	0	0.614	-1.130	-0.889	-1.274	-0.729	18	-0.122	25	0	0

	Features	VIF
9	tags	4.170
7	specialization	3.500
5	pg_view_pv	2.540
3	totalvisits	2.410
0	lead_origin	2.320
11	avail_free_copy	1.930
10	city	1.380
1	lead_source	1.330
4	ttime_on_site	1.220
2	do_not_email	1.100
6	last_activity	1.040
8	curr_occupation	1.040

	Features	VIF
9	tags	4.140
7	specialization	3.500
5	pg_view_pv	2.540
3	totalvisits	2.400
0	lead_origin	2.200
10	avail_free_copy	1.910
1	lead_source	1.320
4	ttime_on_site	1.220
2	do_not_email	1.100
6	last_activity	1.040
8	curr_occupation	1.040

	coef	std err	z	P>\|z\|	[0.025	0.975]
const	-0.5614	0.121	-4.656	0.000	-0.798	-0.325
lead_origin	0.6757	0.058	11.667	0.000	0.562	0.789
lead_source	0.5132	0.035	14.595	0.000	0.444	0.582
do_not_email	-1.4810	0.145	-10.216	0.000	-1.765	-1.197
totalvisits	0.1084	0.044	2.443	0.015	0.021	0.195
ttime_on_site	1.0413	0.034	30.765	0.000	0.975	1.108
pg_view_pv	-0.4758	0.047	-10.069	0.000	-0.568	-0.383
last_activity	0.5183	0.031	16.677	0.000	0.457	0.579
specialization	-0.0415	0.006	-6.841	0.000	-0.053	-0.030
curr_occupation	0.4308	0.036	11.966	0.000	0.360	0.501
tags	0.0096	0.004	2.512	0.012	0.002	0.017
city	0.0398	0.024	1.687	0.092	-0.006	0.086
avail_free_copy	-0.4397	0.073	-5.994	0.000	-0.583	-0.296

	coef	std err	z	P>\|z\|	[0.025	0.975]
const	-0.5380	0.120	-4.492	0.000	-0.773	-0.303
lead_origin	0.6887	0.057	11.986	0.000	0.576	0.801
lead_source	0.5109	0.035	14.541	0.000	0.442	0.580
do_not_email	-1.4786	0.145	-10.196	0.000	-1.763	-1.194
totalvisits	0.1102	0.044	2.484	0.013	0.023	0.197
ttime_on_site	1.0427	0.034	30.828	0.000	0.976	1.109
pg_view_pv	-0.4724	0.047	-10.014	0.000	-0.565	-0.380
last_activity	0.5156	0.031	16.622	0.000	0.455	0.576
specialization	-0.0423	0.006	-6.991	0.000	-0.054	-0.030
curr_occupation	0.4307	0.036	11.954	0.000	0.360	0.501
tags	0.0096	0.004	2.530	0.011	0.002	0.017
avail_free_copy	-0.4317	0.073	-5.899	0.000	-0.575	-0.288

	coef	std err	z	P>\|z\|	[0.025	0.975]
const	-0.5380	0.120	-4.492	0.000	-0.773	-0.303
lead_origin	0.6887	0.057	11.986	0.000	0.576	0.801
lead_source	0.5109	0.035	14.541	0.000	0.442	0.580
do_not_email	-1.4786	0.145	-10.196	0.000	-1.763	-1.194
totalvisits	0.1102	0.044	2.484	0.013	0.023	0.197
ttime_on_site	1.0427	0.034	30.828	0.000	0.976	1.109
pg_view_pv	-0.4724	0.047	-10.014	0.000	-0.565	-0.380
last_activity	0.5156	0.031	16.622	0.000	0.455	0.576
specialization	-0.0423	0.006	-6.991	0.000	-0.054	-0.030
curr_occupation	0.4307	0.036	11.954	0.000	0.360	0.501
tags	0.0096	0.004	2.530	0.011	0.002	0.017
avail_free_copy	-0.4317	0.073	-5.899	0.000	-0.575	-0.288

	Converted	Conv_Prob	ID	predicted	0.000	0.100	0.200	0.300	0.400	0.500	0.600
0	0	0.095	7740	0	1	0	0	0	0	0	0
1	1	0.653	7014	1	1	1	1	1	1	1	1
2	0	0.322	2431	0	1	1	1	1	0	0	0
3	1	0.694	3012	1	1	1	1	1	1	1	1
4	0	0.165	3140	0	1	1	0	0	0	0	0

	lead_origin	lead_source	do_not_email	totalvisits	ttime_on_site	pg_view_pv	last_activity	specialization	curr_occupation	tags	avail_free_copy
7421	1	-1.063	1	2.722	-0.502	0.102	0.639	4	-0.122	2	1
4570	1	-0.392	1	-0.079	-0.409	0.411	1.186	12	-0.122	25	0
5302	0	0.614	0	-1.130	-0.889	-1.274	0.365	18	-0.122	25	0
6471	1	0.949	1	0.971	-0.293	2.096	1.186	13	-0.122	24	1
8958	1	-1.063	0	0.271	0.708	-0.150	-0.729	7	-0.122	0	1

	Features	VIF
9	tags	4.370
7	specialization	3.680
5	pg_view_pv	2.470
3	totalvisits	2.360
0	lead_origin	2.140
10	avail_free_copy	1.940
1	lead_source	1.320
4	ttime_on_site	1.260
2	do_not_email	1.110
6	last_activity	1.040
8	curr_occupation	1.040

	totalvisits	ttime_on_site	pg_view_pv	avail_free_copy_1	city_Other Cities of Maharashtra	curr_occupation_Unemployed	last_activity_Converted to Lead	last_activity_Email Opened	last_activity_Form Submitted on Website	lead_origin_Landing Page Submission	lead_source_Direct Traffic	lead_source_Google	lead_source_Olark Chat	lead_source_Organic Search	tags_Already a student	tags_Not doing further education	tags_Will revert after reading the email	specialization_Finance Management	specialization_Healthcare Management	specialization_Human Resource Management	specialization_IT Projects Management
7740	0.091	0.124	0.167	1.000	1.000	1.000	1.000	0.000	0.000	1.000	1.000	0.000	0.000	0.000	1.000	0.000	0.000	0.000	0.000	1.000	0.000
7014	0.545	0.426	0.333	0.000	0.000	1.000	0.000	1.000	0.000	1.000	0.000	1.000	0.000	0.000	0.000	0.000	1.000	1.000	0.000	0.000	0.000
2431	0.182	0.133	0.333	0.000	0.000	1.000	0.000	0.000	1.000	1.000	0.000	1.000	0.000	0.000	0.000	1.000	0.000	0.000	0.000	0.000	1.000
3012	0.727	0.585	0.667	0.000	0.000	1.000	0.000	1.000	0.000	0.000	0.000	0.000	0.000	1.000	0.000	0.000	1.000	0.000	1.000	0.000	0.000
3140	0.000	0.000	0.000	0.000	0.000	1.000	0.000	1.000	0.000	0.000	0.000	0.000	1.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000

	Features	VIF
0	ttime_on_site	1.650
10	tags_Will revert after reading the email	1.530
1	last_activity_SMS Sent	1.480
9	tags_Ringing	1.120
4	tags_Closed by Horizzon	1.050
7	tags_Lost to EINS	1.040
2	lead_source_Welingak Website	1.030
13	tags_switched off	1.030
6	tags_Interested in other courses	1.020
3	tags_Already a student	1.010
5	tags_Interested in full time MBA	1.010
8	tags_Not doing further education	1.010
11	tags_invalid number	1.010
14	tags_wrong number given	1.010
12	tags_number not provided	1.000

	Converted	Conv_Prob	ID	predicted	0.000	0.100	0.200	0.300	0.400	0.500	0.600	0.700	0.800	0.900
0	0	0.004	7740	0	1	0	0	0	0	0	0	0	0	0
1	1	0.968	7014	1	1	1	1	1	1	1	1	1	1	1
2	0	0.005	2431	0	1	0	0	0	0	0	0	0	0	0
3	1	0.981	3012	1	1	1	1	1	1	1	1	1	1	1
4	0	0.070	3140	0	1	0	0	0	0	0	0	0	0	0

	ttime_on_site	last_activity_SMS Sent	tags_Already a student	tags_Closed by Horizzon	tags_switched off
7421	0.093	0.000	0.000	1.000	0.000
4570	0.116	1.000	0.000	0.000	0.000
5302	0.000	0.000	0.000	0.000	0.000
6471	0.143	1.000	0.000	0.000	1.000
8958	0.385	0.000	1.000	0.000	0.000

	Features	VIF
0	ttime_on_site	1.730
10	tags_Will revert after reading the email	1.590
1	last_activity_SMS Sent	1.550
9	tags_Ringing	1.110
2	lead_source_Welingak Website	1.040
7	tags_Lost to EINS	1.040
4	tags_Closed by Horizzon	1.030
12	tags_switched off	1.030
3	tags_Already a student	1.020
6	tags_Interested in other courses	1.020
5	tags_Interested in full time MBA	1.010
8	tags_Not doing further education	1.010
11	tags_invalid number	1.010

Lead Scoring Case Study¶

Initialization - Preprocessing - Visualization¶

1. Package Imports and Data Initialization¶

----------------------------------------------------------------------¶

2. Data Preprocesing - Part 1¶

Custom Functions for Preprocessing and EDA¶

Data Cleaning, Column Renaming, Missing Value Imputation, Feature Selection¶

Constant Feature Identification¶

¶

----------------------------------------------------------------------¶

3. Data Visualization - EDA¶

Observations¶

¶

Observations¶

The categorical analysis¶

¶

3.1 Other Bivariate - Multivariate plots (computationally intensive)¶

----------------------------------------------------------------------¶

4. Data Preprocesing - Part 2¶

Outlier Analysis and Capping¶

----------------------------------------------------------------------¶

5. Data Imbalance & Conversion Ratio¶

-----------------------------------------------------¶

-----------------------------------------------------¶

Approach - 01¶

Data Encoding¶

Dummy Encoding¶

----------------------------------------------------------------------¶

Train and Test Split¶

----------------------------------------------------------------------¶

Feature Scaling¶

----------------------------------------------------------------------¶

Model Building¶

Custom Functions for Model Training¶

Base Model¶

RFE - Recursive Feature Elimination¶

Model 1¶

Model 2¶

Finding Optimal Cutoff Point¶

----------------------------------------------------------------------¶

ROC Curve and Precision - Recall Curve¶

---------------------------------------------------------------¶

Predictions on the test set¶

Custom Functions for Test¶

Model Validation on Test Data¶

Approach - 01 - Accuracy Score:¶

-----------------------------------------------------¶

-----------------------------------------------------¶

Approach - 02¶

Data Encoding¶

Label Encoding¶

----------------------------------------------------------------------¶

Train and Test Split¶

----------------------------------------------------------------------¶

Feature Scaling¶

----------------------------------------------------------------------¶

Model Building¶

Custom Functions for Model Training¶

Base Model 1¶

RFE - Recursive Feature Elimination¶

Model 1¶

Model 2¶

----------------------------------------------------------------------¶

Finding Optimal Cutoff Point¶

----------------------------------------------------------------------¶

ROC Curve and Precision - Recall Curve¶

----------------------------------------------------------------------¶

Predictions on the test set¶

Custom Functions for Test¶

Model Validation on Test Data¶

Approach - 02 - Accuracy Score:¶

-----------------------------------------------------¶

-----------------------------------------------------¶

Approach - 03¶

Data Encoding¶

Dummy Encoding¶

----------------------------------------------------------------------¶

Train and Test Split¶

----------------------------------------------------------------------¶

Feature Scaling¶